text:
|Publication number
|Publication type
|Application number
|Publication date
|15 Dec 2009
|Filing date
|15 Dec 2004
|Priority date
|24 Apr 2001
|Fee status
|Also published as
|Publication number
|M. Kivanc Mihcak, Ramarathnam Venkatesan, Mariusz H. Jakubowski
|Original Assignee
|Microsoft Corporation
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (111), Non-Patent Citations (51), Referenced by (1), Classifications (46), Legal Events (3)
|External Links: USPTO, USPTO Assignment, Espacenet
This application is a continuation of and claims priority to U.S. patent application Ser. No. 09/843,279, filed Apr. 24, 2001 now U.S. Pat. No. 7,020,775, the disclosure of which is incorporated by reference herein.
This invention generally relates to a technology for deriving robust non-local characteristics and quantizing such characteristics for blind watermarking of a digital good.
"Digital goods" is a generic label for electronically stored or transmitted content. Examples of digital goods include images, audio clips, video, digital film, multimedia, software, and data. Digital goods may also be called a "digital signal," "content signal," "digital bitstream," "media signal," "digital object," "object," and the like.
Digital goods are often distributed to consumers over private and public networks—such as Intranets and the Internet. In addition, these goods are distributed to consumers via fixed computer readable media, such as a compact disc (CD-ROM), digital versatile disc (DVD), soft magnetic diskette, or hard magnetic disk (e.g., a preloaded hard drive).
Unfortunately, it is relatively easy for a person to pirate the pristine digital content of a digital good at the expense and harm of the content owners—which includes the content author, publisher, developer, distributor, etc. The content-based industries (e.g., entertainment, music, film, etc.) that produce and distribute content are plagued by lost revenues due to digital piracy.
Modern digital pirates effectively rob content owners of their lawful compensation. Unless technology provides a mechanism to protect the rights of content owners, the creative community and culture will be impoverished.
Watermarking is one of the most promising techniques for protecting the content owner's rights of a digital good (i.e., digital good). Generally, watermarking is a process of altering the digital good such that its perceptual characteristics are preserved. More specifically, a "watermark" is a pattern of bits inserted into a digital good that may be used to identify the content owners and/or the protected rights.
Watermarks are designed to be completely invisible or, more precisely, to be imperceptible to humans and statistical analysis tools. Ideally, a watermarked signal is perceptually identical to the original signal.
A watermark embedder (i.e., encoder) embeds a watermark into a digital good. It typically uses a secret key to embed the watermark. A watermark detector (i.e., decoder) extracts the watermark from the watermarked digital good.
Blind Watermarking
To detect the watermark, some watermarking techniques require access to the original unmarked digital good or to a pristine specimen of the marked digital good. Of course, these techniques are not desirable when the watermark detector is available publicly. If publicly available, then a malicious attacker may get access to the original unmarked digital good or to a pristine specimen of the marked digital good. Consequently, these types of techniques are not used for public detectors.
Alternatively, watermarking techniques are "blind." This means that they do not require access to the original unmarked digital good or to a pristine specimen of the marked digital good. Of course, these "blind" watermarking techniques are desirable when the watermark detector is publicly available.
Before detection, a watermarked signal may undergo many possible changes by users and by the distribution environment. These changes may include unintentional modifications, such as noise and distortions. Moreover, the marked signal is often the subject of malicious attacks particularly aimed at disabling the detection of the watermark.
Ideally, a watermarking technique should embed detectible watermarks that resist modifications and attacks as long as they result in signals that are of perceptually the same quality. A watermarking technique that is resistant to modifications and attacks may be called "robust." Aspects of such techniques are called "robust" if they encourage such resistance.
Generally speaking, a watermarking system should be robust enough to handle unintentional noise introduction into the signal (such noise may be introduced by A/D and D/A conversions, compressions/decompressions, data corruption during transmission, etc.)
Furthermore, a watermarking system should be robust enough and stealthy enough to avoid purposeful and malicious detection, alternation, and/or deletion of the watermark. Such an attack may use a "shotgun" approach where no specific watermark is known or detected (but is assumed to exist) or may use "sharp-shooter" approach where the specific watermark is attacked.
This robustness problem has attracted considerable attention. In general, the existing robust watermark techniques fall into two categories: spread-spectrum and quantization index modulation (QIM).
With the spread spectrum-type techniques, the watermark indexes the modification to the host data. The host data is the data of the original, unmarked digital signal (i.e., host signal). With typical spread-spectrum watermarking, each bit (e.g., 0s and 1s) of the watermark is embedded into the signal by slightly changing (e.g., adding a pseudorandom sequence that consists of +Δ or +Δ) the signal.
With quantization index modulation (QIM), the watermark is embedded via indexing the modified host data. The modified host data is the data of the marked digital signal (i.e., marked host signal). This is discussed in more detail below.
Those of ordinary skill in the art are familiar with conventional techniques and technology associated with watermarks, watermark embedding, and watermark detecting. In addition, those of ordinary skill in the art are familiar with the typical problems associated with proper watermark detection after a marked signal has undergone changes (e.g., unintentional noise and malicious attacks).
Desiderata of Watermarking Technology
Watermarking technology has several highly desirable goals (i.e., desiderata) to facilitate protection of copyrights of content publishers. Below are listed several of such goals.
Perceptual Invisibility. The embedded information should not induce perceptual changes in the signal quality of the resulting watermarked signal. The test of perceptual invisibility is often called the "golden eyes and ears" test.
Statistical Invisibility. The embedded information should be quantitatively imperceptive for any exhaustive, heuristic, or probabilistic attempt to detect or remove the watermark. The complexity of successfully launching such attacks should be well beyond the computation power of publicly available computer systems. Herein, statistical invisibility is expressly included within perceptual invisibility.
Tamperproofness. An attempt to remove the watermark should damage the value of the digital good well above the hearing threshold.
Cost. The system should be inexpensive to license and implement on both programmable and application-specific platforms.
Non-disclosure of the Original. The watermarking and detection protocols should be such that the process of proving digital good content copyright both in-situ and in-court, does not involve usage of the original recording.
Enforceability and Flexibility. The watermarking technique should provide strong and undeniable copyright proof. Similarly, it should enable a spectrum of protection levels, which correspond to variable digital good presentation and compression standards.
Resilience to Common Attacks. Public availability of powerful digital good editing tools imposes that the watermarking and detection process is resilient to attacks spawned from such consoles.
False Alarms & Misses
When developing a watermarking technique, one does not want to increase the probability of a false alarm. That is when a watermark is detected, but none exists. This is something like finding evidence of a crime that did not happen. Someone may be falsely accused of wrongdoing.
As the probability of false alarms increases, the confidence in the watermarking technique decreases. For example, people often ignore car alarms because they know that more often than not it is a false alarm rather than an actual car theft.
Likewise, one does not want to increase the probability of a miss. This is when the watermark of a signal is not properly detected. This is something like overlooking key piece of evidence at a crime scene. Because of this, a wrongdoing may never be properly investigated. As the probability of misses increases, the confidence in the watermarking technique decreases.
Ideally, the probabilities of a false alarm and a miss are zero. In reality, a compromise is often made between them. Typically, a decrease in the probability of one increases the probability of the other. For example, as the probability of false alarm is decreased, the probability of a miss increases.
Consequently, a watermarking technique is needed that minimizes both while finding a proper balance between them.
Quantization Index Modulation (QIM)
To that end, some have proposed embedding a watermark by indexing the signal (e.g., host data) during the watermark embedding. This technique is called quantization index modulation (QIM) and it was briefly introduced above.
In general, quantization means to limit the possible values of (a magnitude or quantity) to a discrete set of values. Quantization may be thought of as a conversion from non-discrete (e.g., analog or continuous) values to discrete values. Alternatively, it may be a conversion between discrete values with differing scales. Quantization may be accomplished mathematically through rounding or truncation. Typical QIM refers to embedding information by first modulating an index or sequence of indices with the embedded information and then quantizing the host signal with the associated quantizer or sequence of quantizers. A quantizer is class of discontinuous, approximate-identity functions.
The major proponent of such QIM techniques is Brian Chen and Gregory W. Wornell (i.e., Chen-Wornell). In their words, they have proposed, "dither modulation in which the embedded information modulates a dither signal and the host signal is quantized with an associated dither quantizer" (from Abstract of Chen-Wornell article from the IEEE Trans. Inform. Theory).
See the following documents for more details on Chen-Wornell's proposals and on QIM:
However, a key problem with conventional QIM is that it is susceptible to attacks and distortions. Conventional QIM relies upon local characteristics within relative to specific representation of a signal (e.g., in the time or frequency domain). To quantize, conventional QIM relies exclusively upon the values of "individual coefficients" of the representation of the signal. An example of such an "individual coefficients" is the color of an individual pixel of an image.
When quantizing, only the local characteristics of an "individual coefficient" are considered. These local characteristics may include value (e.g., color, amplitude) and relative positioning (e.g., positioning in time and/or frequency domains) of an individual bit (e.g., pixel).
Modifications—from either an attack or some type of unintentional noise—can change local characteristics of a signal quite dramatically. For example, these modifications may have a dramatic affect on the color of a pixel or the amplitude of a bit of sound. However, such modifications have little effect on non-local characteristics of a signal.
Accordingly, a new and robust watermarking technique is needed to find the proper balance between minimizing the probability of false alarms and the probability of misses, such as QIM watermarking techniques. However, such a technique is needed that is less susceptible to attacks and distortions to the local characteristics within a signal.
Described herein is a technology for deriving robust non-local characteristics and quantizing such characteristics for blind watermarking of a digital good.
This technology finds the proper balance between minimizing the probability of false alarms (i.e., detecting a non-existent watermark) and the probability of misses (i.e., failing to detect an existing watermark). One possible technique is quantization index modulation (QIM) watermarking. However, conventional QIM is susceptible to attacks and distortions to the local characteristics of a digital good.
The technology, described herein, performs QIM based upon non-local characteristics of the digital good. Non-local characteristics may include statistics (e.g., averages, median) of a group of individual parts (e.g., pixels) of a digital good.
This summary itself is not intended to limit the scope of this patent. Moreover, the title of this patent is not intended to limit the scope of this patent. For a better understanding of the present invention, please see the following detailed description and appending claims, taken in conjunction with the accompanying drawings. The scope of the present invention is pointed out in the appending claims.
The same numbers are used throughout the drawings to reference like elements and features.
In the following description, for purposes of explanation, specific numbers, materials and configurations are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one skilled in the art that the present invention may be practiced without the specific exemplary details. In other instances, well-known features are omitted or simplified to clarify the description of the exemplary implementations of present invention, thereby better explain the present invention. Furthermore, for ease of understanding, certain method steps are delineated as separate steps; however, these separately delineated steps should not be construed as necessarily order dependent in their performance.
The following description sets forth one or more exemplary implementations of a Derivation and Quantization of Robust Non-Local Characteristics for Blind Watermarking that incorporate elements recited in the appended claims. These implementations are described with specificity in order to meet statutory written description, enablement, and best-mode requirements. However, the description itself is not intended to limit the scope of this patent.
The inventors intend these exemplary implementations to be examples. The inventors do not intend these exemplary implementations to limit the scope of the claimed present invention. Rather, the inventors have contemplated that the claimed present invention might also be embodied and implemented in other ways, in conjunction with other present or future technologies.
An example of an embodiment of a Derivation and Quantization of Robust Non-Local Characteristics for Blind Watermarking may be referred to as "exemplary non-local QIM watermarker."
Incorporation by Reference
The following co-pending patent applications are incorporated by reference herein (which are all assigned to the Microsoft Corportaion):
The one or more exemplary implementations, described herein, of the present claimed invention may be implemented (in whole or in part) by a non-local QIM watermarking architecture 100 and/or by a computing environment like that shown in
In general, the exemplary non-local QIM watermarker derives robust non-local characteristics of a digital good. It quantizes such characteristics for blind watermarking of the digital good.
The exemplary non-local QIM watermarker minimizes the probability of false alarms (i.e., detecting a non-existent watermark) and the probability of misses (i.e., failing to detect an existing watermark). It does so by employing quantization index modulation (QIM) watermarking. However, it does not employ conventional QIM techniques because they are susceptible to attacks and distortions to the local characteristics of a digital good.
Local Characteristics
Conventional QIM relies upon local characteristics within a signal (i.e., a digital good). To quantize, conventional QIM relies exclusively upon the values of "individual elements" of the host signal. When quantizing, only the local characteristics of an "individual element" are considered. These local characteristics may include value (e.g., color, amplitude) and relative positioning (e.g., positioning in time and/or frequency domains) of an individual bit (e.g., pixel).
Modifications—from either an attack or unintentional noise—can change local characteristics of a signal quite dramatically. For example, these modifications may have a dramatic affect on the color of a pixel or the amplitude of a bit of sound. However, such modifications have little effect on non-local characteristics of a signal.
Non-Local Characteristics
Non-local characteristics are representative of general characteristics of a group or collection of individual elements. Such a group may be called a segment. Non-local characteristics are not representative of the individual local characteristics of the individual elements; rather, they are representative of the group (e.g., segments) as a whole.
The non-local characteristics may be determined by a mathematical or statistical representation of a group. For example, it may be an average of the color values of all pixels in a group. Consequently, such non-local characteristics may also be called "statistical characteristics." Local characteristics do not have statistical characteristics because a fixed value for a given category. Thus, no statistics are derived from a single value.
The non-local characteristics are not local characteristics. They are not global characteristics. Rather, they are in between. Consequently, they may also be called "semi-global" characteristics.
Brief Overview
Given an original, unmarked good, the exemplary non-local QIM watermarker derives robust characteristics that are not local in nature. For example, the exemplary non-local QIM watermarker may employ randomized non-invertible transforms to produce robust non-local characteristics that can be modified without perceptual distortion. These characteristics are typically represented statistically and/or mathematically.
To embed the watermark, the exemplary non-local QIM watermarker performs quantization of these non-local characteristics in one or more dimensional lattices or vector spaces. The marked good that results from the exemplary non-local QIM watermarker is robust against unintentional and intentional modifications (e.g., malicious attacks). Examples of malicious attacks include de-synching, random bending, and many other benchmark attacks (e.g., Stirmark attacks).
Exemplary Non-Local QIM Watermarking Architecture
The watermark embedding system 132 applies the watermark to a digital signal from the content storage 130. Typically, the watermark identifies the content producer 122, providing a signature that is embedded in the signal and cannot be cleanly removed.
The content producer/provider 122 has a distribution server 134 that distributes the watermarked content over the network 124 (e.g., the Internet). A signal with a watermark embedded therein represents to a recipient that the signal is being distributed in accordance with the copyright authority of the content producer/provider 122. The server 134 may further compress and/or encrypt the content conventional compression and encryption techniques prior to distributing the content over the network 124.
Typically, the client 126 is equipped with a processor 140, a memory 142, and one or more content output devices 144 (e.g., display, sound card, speakers, etc.). The processor 140 runs various tools to process the marked signal, such as tools to decompress the signal, decrypt the date, filter the content, and/or apply signal controls (tone, volume, etc.). The memory 142 stores an operating system 150 (such as a Microsoft� Windows 2000� operating system), which executes on the processor. The client 126 may be embodied in many different ways, including a computer, a handheld entertainment device, a set-top box, a television, an appliance, and so forth.
The operating system 150 implements a client-side watermark detecting system 152 to detect watermarks in the digital signal and a content loader 154 (e.g., multimedia player, audio player) to facilitate the use of content through the content output device(s) 144. If the watermark is present, the client can identify its copyright and other associated information.
The operating system 150 and/or processor 140 may be configured to enforce certain rules imposed by the content producer/provider (or copyright owner). For instance, the operating system and/or processor may be configured to reject fake or copied content that does not possess a valid watermark. In another example, the system could load unverified content with a reduced level of fidelity.
Exemplary Non-Local QIM Watermark Embedding System
The watermark embedding system 200 includes an amplitude normalizer 210, a transformer 220, a partitioner 230, segment-statistics calculator 240, a segment quantizer 250, a delta-sequence finder 260, and a signal marker 270.
The amplitude normalizer 210 obtains a digital signal 205 (such as an audio clip). It may obtain the signal from nearly any source, such as a storage device or over a network communications link. As its name implies, it normalizes the amplitude of the signal.
The transformer 220 receives the amplitude-normalized signal from the normalizer 210. The transformer 220 puts the signal in canonical form using a set of transformations. Specifically, discrete wavelet transformation (DWT) may be employed (particularly, when the input is an image) since it compactly captures significant signal characteristics via time and frequency localization. Other transformations may be used. For instance, shift-invariant and shape-preserving "complex wavelets" and any overcomplete wavelet representation or wavelet packet are good candidates (particularly for images).
The transformer 220 also finds the DC subband of the initial transformation of the signal. This DC subband of the transformed signal is passed to the partitioner 230.
The partitioner 230 separates the transformed signal into multiple, pseudorandomly sized, pseudorandomly positioned, adjacent, non-contiguous segments (i.e., partitions). A secret key K is the seed for pseudorandom number generation here. This same K may be used to reconstruct the segments by an exemplary non-local QIM watermark detecting system 400.
For example, if the signal is an image, it might be partitioned into two-dimensional polygons (e.g., rectangles) of pseudorandom size and location. In another example, if the signal is an audio clip, a two-dimensional representation (using frequency and time) of the audio clip might be separated into two-dimensional polygons (e.g., triangles) of pseudorandom size and location.
In this implementation, the segments do not overlap. They are adjacent and non-contiguous. In alternative implementations, the segments may be overlapping.
For each segment, the segment-statistics calculator 240 calculates statistics of the multiple segments generated by the partitioner 230. Statistics for each segment are calculated. These statistics may be, for example, any finite order moments of a segment. Examples of such include the mean, the median, and the standard deviation.
Generally, the statistics calculations for each segment are independent of the calculations of other segments. However, other alternatives may involve calculations dependent on data from multiple segments.
A suitable statistic for such calculation is the mean (e.g., average) of the values of the individual bits of the segment. Other suitable statistics and their robustness are discussed in Venkatesan, Koon, Jakubowski, and Moulin, "Robust image hashing," Proc. IEEE ICIP 2000, Vancouver, Canada, September 2000. In this document, no information embedding was considered, but similar statistics were discussed.
For each segment, the segment quantizer 250 applies a multi-level (e.g., 2, 3, 4) quantization (i.e., high-dimensional, vector-dimensional, or lattice-dimensional quantization) on the output of the segment-statistics calculator 240 to obtain quantitized data. Of course, other levels of quantization may be employed. The quantizer 250 may be adaptive or non-adaptive.
This quantization may be done randomly also. This may be called randomized quantization (or randomized rounding). This means that the quantizer may randomly decide to round up or round down. It may do it pseudorandomly (using the secret key). This adds an additional degree of robustness and helps hide the watermark.
The delta-sequence finder 260 finds a pseudorandom sequence Z that estimates the difference (i.e., delta) between the original transformed signal X and the combination of segments of quantized statistics. The pseudorandom sequence Z may also be called the delta-sequence. For example, if the statistic is averaging, then Z s.t. Avgi(X+Z)=Avgi({circumflex over (X)})={circumflex over (μ)}i, where {circumflex over (X)} is the marked signal and {circumflex over (μ)}i is average for a segment.
When finding a delta-sequence Z, it is desirable to minimize the perceptual distortion; therefore, some perceptual distortion metrics may be employed to create this sequence. Thus, in creating Z for, the criteria may include a combination of that minimizes the visual distortions on X+Z (compared to {circumflex over (X)}) and minimizes the distance between the statistics of X+Z and quantized statistics of X.
The signal marker 270 marks the signal with delta-sequence Z so that {circumflex over (X)}=X+Z. The signal marker may mark the signal using QIM techniques. This marked signal may be publicly distributed to consumers and clients.
The functions of aforementioned components of the exemplary non-local QIM watermark embedding system 200 of
Methodological Implementation of the Exemplary Non-Local QIM Watermark Embedding
At 310 of
At 314, the exemplary non-local QIM watermarker partitions the transformed signal into multiple, pseudorandomly sized, pseudorandomly positioned, adjacent, non-contiguous segments. A secret key K is the seed for pseudorandom number generation here. This same K may be used to reconstruct the segments in the watermark detecting process.
For example, if the signal is an image, it might be separated into two-dimensional polygons (e.g., rectangles) of pseudorandom size and location. In another example, if the signal is an audio clip, a two-dimensional representation (using frequency and time) of the audio clip might be separated into two-dimensional polygons (e.g., trapezoids) of pseudorandom size and location.
In this implementation, the segments do not overlap. They are adjacent and non-contiguous. In alternative implementations, the segments may be overlapping.
For each segment of the signal, the exemplary non-local QIM watermarker repeats blocks 320 through 330. Thus, each segment is processed in the same manner.
At 322 of
At 324, the exemplary non-local QIM watermarker finds a pseudorandom sequence Z where the statistics calculation (e.g., averaging) of this sequence combined with the segmented signal generates the calculation of watermarked signal segment. This is also equal to the quanitized statistics of the signal segment.
At 330, the process loops back to 320 for each unprocessed segment. If all segments have been processed, then it proceeds to 340.
At 340 of
Exemplary Non-Local QIM Watermark Detecting System
The watermark detecting system 400 includes an amplitude normalizer 410, a transformer 420, a partitioner 430, segment-statistics calculator 440, a segment MAP decoder 450, a watermark-presence determiner 460, a presenter 470, and a display 480.
The amplitude normalizer 410, the transformer 420, the partitioner 430, and the segment-statistics calculator 440 of the watermark detecting system 400 of
For each segment, the segment MAP decoder 450 determines a quantized value using the MAP decoding scheme. In general, MAP techniques involve finding (and possibly ranking by distance) all objects (in this instance, quantized values) in terms of their distance from a "point." In this example, the "point" is the statistics calculated for a given segment. Nearest-neighbor decoding is one specific instance of MAP decoding. Those of ordinary skill in the art understand and appreciate MAP and nearest-neighbor techniques.
In addition, the segment MAP decoder 450 determines a confidence factor based upon the distance between the quanitized values and the statistics calculated for a given segment. If they are coexistent, then factor will indicate a high degree of confidence. If they are distant, then it may indicate a low degree of confidence. Furthermore, the decoder 450 combines the confidence factors of the segments to get an overall confidence factor. That combination may be most any statistical combination (e.g., addition, average, median, standard deviation, etc.).
The watermark-presence determiner 460 determines whether a watermark is present. The determiner may use some distortion metric d(w,ŵ) and some threshold T to decide if watermark is present or not. A normalized Hamming distance may be used.
The presenter 470 presents one of three indications: "watermark present," "watermark not present," and "unknown." If the confidence factor is low, it may indicate "unknown." In addition, it may present the confidence-indication value.
This information is presented on the display 480. Of course, this display may be any output device. It may also be a storage device.
The functions of aforementioned components of the exemplary non-local QIM watermark detecting system 400 of
Methodological Implementation of the Exemplary Non-Local QIM Watermark Detecting
At 510 of
At 512, it finds a transform of the amplitude-normalized signal and gets significant frequency subband. That may be a low or the lowest subband (e.g., the DC subband). Generally, the subband selected may be one that represents the signal is a manner that helps further robust watermarking and detection of the watermark. The lower frequency subbands are suitable because they tend to remain relatively invariant after signal perturbation.
The result of this block is a transformed signal. When watermarking an image, an example of a suitable transformation is discrete wavelet transformation (DWT). When watermarking an audio clip, an example of a suitable transformation is MCLT (Modulated Complex Lapped Transform). However, most any other similar transformation may be performed in alternative implementations.
At 514, the exemplary non-local QIM watermarker partitions the transformed signal into multiple, pseudorandomly sized, pseudorandomly positioned, adjacent, non-contiguous segments. It uses the same secret key K is the seed for its pseudorandom number generation here. Therefore, this process generates the same segments as in the embedding process.
For each segment of the signal, the exemplary non-local QIM watermarker repeats blocks 520 through 530. Thus, each segment is processed in the same manner.
At 522 of
At 526, it measures how close each decoded value is to a quanitized value and tracks such measurements. The data provided by such measurements may provide an indication of the confidence of a resulting watermark-presence determination.
At 530, the process loops back to 520 for each unprocessed segment. If all segments have been processed, then it proceeds to 540.
At 540 of
At 544, based upon the watermark-presence determination of block 540 and the confidence-indication of block 544, the exemplary non-local QIM watermarker provides one of three indications: watermark present, watermark not present, and unknown. In addition, it may present the confidence-indication value.
At 550, the process ends.
Probability of False Alarms and Misses
There is a relationship between Probability of false alarm (PF), Probability of miss (PM), and the average sizes of the segments. PF is the probability of declaring that a watermark is present even though it is not. PM is the probability declaring that a watermark is not present although it is present indeed. Generally, the average segment size is relatively directly proportional to the PF, but it is relatively indirectly proportional to the PM.
For example, if the average segment-size is extremely small. So small that they are equivalent to the individual bits (e.g., equal to a pixel in an image. In that situation, watermarks are embedded in single coefficients, which is equivalent to the conventional schemes based on local characteristics. For such a case, PF is very small and PM is high. Conversely, if the average segment-size is extremely large. So large that it is the maximum size of the signal (e.g., the whole image). In that situation, PM is presumably very low whereas PF is high.
Other Implementation Details
For the following descriptions of an implementation of the exemplary non-local QIM watermarker, assume the following:
The input signal X is an image. Of course, for signal may be other types for other implementations. However, for this example implementation, the signal is an image. Let w be the watermark (a binary vector) to be embedded in X and a random binary string K be the secret key. The output of a watermark encoder, {circumflex over (X)} is the watermarked image in which the information w is hidden via the secret key K. This image will possibly undergo various attacks, yielding X. A watermark decoder, given {circumflex over (X)} and the secret key K outputs w. It is required that {circumflex over (X)} and X are approximately the same for all practical purposes and that {circumflex over (X)} is still of acceptable quality. The decoder uses some distortion metric d(w, ŵ) and some threshold T to decide if watermark is present or not. The exemplary non-local QIM watermarker use a normalized Hamming distance, which is the ratio of the usual Hamming distance and the length of the inputs.
WM via Quantization
The exemplary non-local QIM watermarker computes a vector μ by applying a forward transformation TF on X. The exemplary non-local QIM watermarker assumes that X is a grayscale image; if not, a linear transformation may be applied on a colored image to obtain the intensity image. (For colored images, most of the energy is concentrated in the intensity plane). Once the information hiding process is done via quantization, there is {circumflex over (μ)}, and the watermarked data {circumflex over (X)} are obtained by applying the transform TR on {circumflex over (μ)}. The exemplary non-local QIM watermarker randomizes the process using a random string derived from our secret key K as a seed to a pseudo-random generator.
The scheme is generic. So most any transform may be used. In addition, TF=(TR)−1 need not be true, or even that their inverses exist. The exemplary non-local QIM watermarker does not constrain the space where quantization occurs. The decoder applys the transform TF on the input, Y, to get the output is μY and applying approximate Maximum Likelihood (ML) estimation of the possibly embedded sequence wY. Through a pseudo-random generator, K will determine many randomization functions of the transform, quantization and estimation stages. Once ŵY is found, it is compared with the embedded watermark μi, if the distance between them is close to 0 (or less than a threshold T), then it is declared that the watermark is present; otherwise not present. This contrasts with the natural measure for spread spectrum techniques that yields a high value of correlation if the watermark is present.
More Details of this Methodological Implementation
The transforms TF and TR help enhance robustness. For TF to retain significant image characteristics, we may use discrete wavelet transformation (DWT) at the initial stage. Next, semi-global (i.e., non-local) statistics of segments are determined of the image. The local statistics are not robust.
When the exemplary non-local QIM watermarker computes, for example, the first order statistics of an original image signal and computes several attacked versions for random rectangles of fixed size. Then the average mean squared error between these statistics may be found. The error monotonically decays as the size of the rectangles is increased.
In TF, μ is set to be the estimated first order statistics of randomly chosen rectangles in the wavelet domain. Here TF is not invertible if the number of rectangles is less than the number of coefficients. To choose TR, one might first generate a pseudo-random sequence p in the image domain that has the property to be visually approximately unnoticeable, pass it through TF, find the corresponding statistics μp, compute the necessary scaling factors α such that the pseudorandom sequence p scaled by α and added to X yields the averages {circumflex over (μ)} that is quantized μ. This implementation uses randomly chosen non-overlapping rectangles. The exemplary non-local QIM watermarker defines two quantizers, Q0 and Q1, which map vectors in Rn to some nearby chosen lattice points.
Here is a formal description. Define Qi:={(qij, Sij)|jεZ}; qijεRn to be reconstruction points for Qi; Sij⊂ are the corresponding quantization bins; iε{0,1}, jεZ. For each j, then S0j∩[(∪k≠jS0k)∪(∪kS1k)]=S1j∩[(∪k≠jS1k)∪(∪kS0k)]=�. Here q0j, q1j are determined in a pseudo-random fashion (using K as the seed of the random number generator) and n is the dimension of quantization. The exemplary non-local QIM watermarker defines Q0[α]=q0j αεS0j, and likewise for Q1. Both Q0 and Q1 are derived from a single quantizer, Q, such that (a) the set of reconstruction points of Q is equal to {q0j}j∪{q1j}j and (b) for all j, k, minlεZdL
If, for example, n=1. The exemplary non-local QIM watermarker finds {E(q0j)} and {E(q1j)} meeting the requirements mentioned above. Then randomization regions that are neighborhoods in Rn around {E(q0j)} and {E(q1j)} are introduced. Then {q0j⇄} and {q1j} are randomly choosen from these regions using some suitable probability distributions. The sizes of the randomizing regions and their shapes are input parameters. Let nR be the number of rectangles each indexed suitably. Let R be the length of the binary watermark vector μi to be hidden in rectangles. For a vector, v denotes its i-th entry by v(i). Let L denote the number of levels of DWT that is applied.
Let N be the number of pixels in X and S:=[s1, . . . , sN] be the vector that consists of pixels of X sorted in ascending order. Create subvectors sL:=[st, . . . , st′] where t:=round(N(1−β)γ) and t′=round (N(1+β)γ) and sH:=[su, su′] where u=round(N[1−(1−β)γ]) and u′=round(N[(1+β)γ]). Here round(r) equals the nearest integer to r and the system parameters 0<β, γ<<1. Let m and M be the mean values of the elements of S L and S H respectively. Apply the point operator P[x]=255*(x−m)/(M−m) to each element of X to get X′.
Find the L-level DWT of X′. Let X A be its DC subband.
Partition the XA into random non overlapping nR rectangles; calculate μ such that μ(i) is the mean value of the coefficients within rectangle i, i≦nR.
Let μi:=[μ(n(i−1)+1), . . . , μ(ni−1), μ(ni)], i≦R. Use Q0 and Q1 to quantize: for i≦R, if wi (i)=0, quantize μi using Q0 else quantize μi using Q1. Concatenating the quantized {μi} we get {circumflex over (μ)}.
Find the differences between the original statistics and the quantized statistics: d={circumflex over (μ)}−μ.
Generate a pseudo-random sequence p=[pij] in the spatial (i.e. the original image) domain as follows: Choose pij randomly and uniformly from {0, 1} if st′<Xij<st, where t′=round(Nγ) and t=round(N(1−γ)); otherwise set pij=0. Now apply L-level DWT to the matrix p, extract the DC subband of the output and call it pW. Now compute the corresponding statistics similar to the step
Compute the scaling factors α such that α(i)=d (i)/ μp(i), i≦nR.
For each rectangle whose index is i, multiply all coefficients of p W within that rectangle by α(i), let the resulting vector be {circumflex over (p)}W.
Apply inverse DWT on {circumflex over (p)}W, let the output be {circumflex over (p)}.
Compute the watermarked data: Xij=X+{circumflex over (p)}. Apply the inverse of the point operator (see step 1) on Xnij to get {circumflex over (X)}.
Decoding (Input Y):
Similar to first part of the encoding above, find the corresponding point operator on Y. Now apply the operator to Y and to the output apply L-level DWT, extract the DC subband and call it YA.
Applying the partitioning procedure of the encoding above to Y and find its statistics μY. Let μY(i) be the ith element.
Using Q0 and Q1, now is described how to carry out approximate ML estimation to find the decoded sequence wYi: Let μY i:=[μiY(n(i−1)+1), . . . , μY(ni−1), μY(ni)]. For rectangles indexed by n(i−1)+1, . . . , ni−1, ni, i≦R, let r0(i) be the closest point to μY i among reconstruction points of Q0; likewise let r1(i) be the closest point to μ Y i among reconstruction points of Q1. If dl2(μY i, r0(i))<dl2(μY i, r1(i)) then assign wiY(i)=0; otherwise μw iY(i)=1.
Compute d(μiY, μi). If the result is less than threshold T, declare that the watermark was detected, or otherwise not present.
Exemplary Computing System and Environment
The exemplary computing environment 900 is only one example of a computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the computer and network architectures. Neither should the computing environment 900 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary computing environment 900.
The exemplary non-local QIM watermarker may be implemented with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use include, but are not limited to, personal computers, server computers, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The exemplary non-local QIM watermarker may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The exemplary non-local QIM watermarker may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The computing environment 900 includes a general-purpose computing device in the form of a computer 902. The components of computer 902 can include, by are not limited to, one or more processors or processing units 904, a system memory 906, and a system bus 908 that couples various system components including the processor 904 to the system memory 906.
The system bus 908 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures can include an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnects (PCI) bus also known as a Mezzanine bus.
Computer 902 typically includes a variety of computer readable media. Such media can be any available media that is accessible by computer 902 and includes both volatile and non-volatile media, removable and non-removable media.
The system memory 906 includes computer readable media in the form of volatile memory, such as random access memory (RAM) 910, and/or non-volatile memory, such as read only memory (ROM) 912. A basic input/output system (BIOS) 914, containing the basic routines that help to transfer information between elements within computer 902, such as during start-up, is stored in ROM 912. RAM 910 typically contains data and/or program modules that are immediately accessible to and/or presently operated on by the processing unit 904.
Computer 902 may also include other removable/non-removable, volatile/non-volatile computer storage media. By way of example,
The disk drives and their associated computer-readable media provide non-volatile storage of computer readable instructions, data structures, program modules, and other data for computer 902. Although the example illustrates a hard disk 916, a removable magnetic disk 920, and a removable optical disk 924, it is to be appreciated that other types of computer readable media which can store data that is accessible by a computer, such as magnetic cassettes or other magnetic storage devices, flash memory cards, CD-ROM, digital versatile disks (DVD) or other optical storage, random access memories (RAM), read only memories (ROM), electrically erasable programmable read-only memory (EEPROM), and the like, can also be utilized to implement the exemplary computing system and environment.
Any number of program modules can be stored on the hard disk 916, magnetic disk 920, optical disk 924, ROM 912, and/or RAM 910, including by way of example, an operating system 926, one or more application programs 928, other program modules 930, and program data 932. Each of such operating system 926, one or more application programs 928, other program modules 930, and program data 932 (or some combination thereof) may include an embodiment of an amplitude normalizer, a transformer, a partitioner, a segment-statistics calculator, a segment quantizer, an delta-sequence finder, and a signal marker.
A user can enter commands and information into computer 902 via input devices such as a keyboard 934 and a pointing device 936 (e.g., a "mouse"). Other input devices 938 (not shown specifically) may include a microphone, joystick, game pad, satellite dish, serial port, scanner, and/or the like. These and other input devices are connected to the processing unit 904 via input/output interfaces 940 that are coupled to the system bus 908, but may be connected by other interface and bus structures, such as a parallel port, game port, or a universal serial bus (USB).
A monitor 942 or other type of display device can also be connected to the system bus 908 via an interface, such as a video adapter 944. In addition to the monitor 942, other output peripheral devices can include components such as speakers (not shown) and a printer 946 which can be connected to computer 902 via the input/output interfaces 940.
Computer 902 can operate in a networked environment using logical connections to one or more remote computers, such as a remote computing device 948. By way of example, the remote computing device 948 can be a personal computer, portable computer, a server, a router, a network computer, a peer device or other common network node, and the like. The remote computing device 948 is illustrated as a portable computer that can include many or all of the elements and features described herein relative to computer 902.
Logical connections between computer 902 and the remote computer 948 are depicted as a local area network (LAN) 950 and a general wide area network (WAN) 952. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets, and the Internet.
When implemented in a LAN networking environment, the computer 902 is connected to a local network 950 via a network interface or adapter 954. When implemented in a WAN networking environment, the computer 902 typically includes a modem 956 or other means for establishing communications over the wide network 952. The modem 956, which can be internal or external to computer 902, can be connected to the system bus 908 via the input/output interfaces 940 or other appropriate mechanisms. It is to be appreciated that the illustrated network connections are exemplary and that other means of establishing communication link(s) between the computers 902 and 948 can be employed.
In a networked environment, such as that illustrated with computing environment 900, program modules depicted relative to the computer 902, or portions thereof, may be stored in a remote memory storage device. By way of example, remote application programs 958 reside on a memory device of remote computer 948. For purposes of illustration, application programs and other executable program components such as the operating system are illustrated herein as discrete blocks, although it is recognized that such programs and components reside at various times in different storage components of the computing device 902, and are executed by the data processor(s) of the computer.
Computer-Executable Instructions
An implementation of an exemplary non-local QIM watermarker may be described in the general context of computer-executable instructions, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.
Exemplary Operating Environment
The operating environment is only an example of a suitable operating environment and is not intended to suggest any limitation as to the scope or use of functionality of the exemplary non-local QIM watermarker(s) described herein. Other well known computing systems, environments, and/or configurations that are suitable for use include, but are not limited to, personal computers (PCs), server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, programmable consumer electronics, wireless phones and equipments, general- and special-purpose appliances, application-specific integrated circuits (ASICs), network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
Computer Readable Media
An implementation of an exemplary non-local QIM watermarker may be stored on or transmitted across some form of computer readable media. Computer readable media can be any available media that can be accessed by a computer. By way of example, and not limitation, computer readable media may comprise "computer storage media" and "communications media."
"Computer storage media" include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer.
"Communication media" typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as carrier wave or other transport mechanism. Communication media also includes any information delivery media.
The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also included within the scope of computer readable media.
Although the invention has been described in language specific to structural features and/or methodological steps, it is to be understood that the invention defined in the appended claims is not necessarily limited to the specific features or steps described. Rather, the specific features and steps are disclosed as preferred forms of implementing the claimed invention.
|Cited Patent
|Filing date
|Publication date
|19 Nov 1985
|20 Sep 1988
|International Business Machines Corporation
|Information processing system for compaction and replacement of phrases
|26 Dec 1990
|3 Mar 1992
|Hughes Aircraft Company
|Pattern recognition apparatus utilizing area linking and region growth techniques
|2 May 1990
|11 May 1993
|Broadcast Data Systems Limited Partnership
|Signal recognition system and method
|8 Dec 1993
|27 Sep 1994
|International Business Machines Corporation
|Generalized shape autocorrelation for shape acquisition and recognition
|22 Jan 1992
|13 Jun 1995
|Alphanet Telecom Inc.
|Facsimile arrangement
|1 Apr 1994
|7 Nov 1995
|Ricoh Company, Ltd.
|Image matching and retrieval by multi-access redundant hashing
|6 May 1993
|13 Feb 1996
|Hutson; William H.
|Method and system to enhance medical signals for real-time analysis and high-resolution display
|15 Oct 1992
|9 Jul 1996
|Digital Equipment Corporation
|Void and cluster apparatus and method for generating dither templates
|7 Jun 1995
|18 Mar 1997
|The Dice Company
|Steganographic method and device
|17 Oct 1995
|2 Sep 1997
|Northern Telecom Limited
|Method of building fast MACS from hash functions
|31 Dec 1996
|11 Nov 1997
|The Dice Company
|Steganographic method and device
|25 May 1995
|18 Nov 1997
|International Business Machines Corporation
|Method and apparatus for updating and transmitting line-oriented data sets
|15 Jul 1994
|31 Mar 1998
|Lucent Technologies, Inc.
|Method of incorporating a variable rate auxiliary data stream with a variable rate primary data stream
|7 Jun 1995
|30 Jun 1998
|United Parcel Service Of America, Inc.
|Method and system for comparing strings with entries of a lexicon
|4 Jun 1996
|1 Sep 1998
|Multex Systems, Inc.
|Information delivery system and method
|19 Sep 1996
|15 Sep 1998
|Panasonic Technologies, Inc.
|Method of locating a penstroke sequence in a computer
|26 Jun 1996
|10 Nov 1998
|Xerox Corporation
|Representing a region of a color image using a space-color separable model
|16 May 1996
|19 Jan 1999
|Digimarc Corporation
|Methods for surveying dissemination of proprietary empirical data
|30 Sep 1996
|23 Feb 1999
|Kaman Sciences Corporation
|Pixel hashing image recognition system
|16 Oct 1996
|4 May 1999
|Microsoft Corporation
|Iterative convolution filter particularly suited for use in an image classification and retrieval system
|26 Aug 1996
|22 Jun 1999
|Philips Electronics North America Corporation
|Using index keys extracted from JPEG-compressed images for image retrieval
|21 Jul 1997
|29 Jun 1999
|Muscle Fish
|Method and article of manufacture for content-based analysis, storage, retrieval, and segmentation of audio information
|19 Jun 1997
|14 Sep 1999
|Xerox Corporation
|Method of indexing words in handwritten document images using image hash tables
|16 Oct 1997
|9 Nov 1999
|Intellectual Protocols, L.L.C.
|Web site copyright registration system and method
|30 Sep 1996
|13 Jun 2000
|Microsoft Corporation
|Segmentation of image features using hierarchical analysis of multi-valued image data and weighted averaging of segmentation results
|28 May 1997
|27 Jun 2000
|Symantec Corporation
|System for supporting secured log-in of multiple users into a plurality of computers using combined presentation of memorized password and transportable passport record
|8 Dec 1997
|8 Aug 2000
|The United States Of America As Represented By The Secretary Of The Air Force
|Digital watermarking by adding random, smooth patterns
|4 Jun 1998
|10 Oct 2000
|Hitachi Ltd.
|Digital data authentication method
|19 Mar 1999
|12 Jun 2001
|International Business Machines Corporation
|Compression-tolerant watermarking scheme for image authentication
|30 May 1997
|19 Jun 2001
|Enroute, Inc
|Combining digital images based on three-dimensional relationships between source image data sets
|31 Jan 2000
|21 Aug 2001
|Yamaha Corporation
|Vector quantizer and vector quantization method
|21 May 1998
|6 Nov 2001
|Massachusetts Institute Of Technology
|System, method, and product for information embedding using an ensemble of non-intersecting embedding generators
|2 Sep 1999
|20 Nov 2001
|Xerox Corporation
|Method for creating a geometric hash tree in a document processing system
|30 Jun 1998
|11 Dec 2001
|At&T Corp.
|Method and apparatus for watermarking digital bitstreams
|3 Nov 1998
|26 Mar 2002
|Ricoh Co., Ltd.
|Compressed document matching
|15 Apr 1998
|26 Mar 2002
|Intel Corporation
|Method and apparatus for protecting flash memory
|7 Apr 1998
|9 Apr 2002
|International Business Machines Corporation
|Multiple frame data hiding method and data detection method
|7 Nov 1997
|23 Apr 2002
|Microsoft Corporation
|Automatic word completion system for partially entered data
|19 Jul 2000
|7 May 2002
|Digimarc Corporation
|Wavelet domain watermarks
|2 Mar 2000
|4 Jun 2002
|Amazon.Com Holdings, Inc
|System and method for correcting spelling errors in search queries using both matching and non-matching search terms
|10 Jun 1999
|9 Jul 2002
|Oracle International Corporation
|System for efficient content-based retrieval of images
|19 Jul 2000
|23 Jul 2002
|Kowa Co., Ltd.
|Watermark applied to one-dimensional data
|5 Nov 1998
|5 Nov 2002
|Matsushita Electric Industrial Co., Ltd.
|Apparatus for and method of embedding and extracting digital information and medium having program for carrying out the method recorded thereon
|21 Jan 1999
|28 Jan 2003
|Canon Kabushiki Kaisha
|Electronic watermarking method, electronic information distribution system, image filing apparatus and storage medium therefor
|30 Mar 1999
|18 Feb 2003
|Wistaria Trading, Inc.
|Optimization methods for the insertion, protection, and detection of digital watermarks in digitized data
|22 Jan 1999
|11 Mar 2003
|The Trustees Of Columbia University In The City Of New York
|Method and apparatus for image authentication
|7 Sep 1999
|8 Apr 2003
|Microsoft Corporation
|Technique for detecting a watermark in a marked image
|7 Sep 1999
|3 Jun 2003
|Microsoft Corporation
|Technique for watermarking an image and a resulting watermarked image
|22 Jan 1999
|3 Jun 2003
|Kent Ridge Digital Labs
|Method and apparatus for indexing and retrieving images using visual keywords
|25 Feb 2000
|24 Jun 2003
|Eastman Kodak Company
|Method and system for search and retrieval of similar patterns
|12 Sep 1997
|23 Sep 2003
|Purdue Research Foundation
|Authentication of signals using watermarks
|12 Oct 1999
|30 Sep 2003
|Digimarc Corporation
|Image marking with pixel modification
|7 Sep 2000
|11 Nov 2003
|Digimarc Corporation
|Method for monitoring internet dissemination of image, video, and/or audio files
|8 May 2001
|25 Nov 2003
|Sunflare Co., Ltd.
|Probabilistic information retrieval based on differential latent semantic space
|24 Jan 2001
|2 Dec 2003
|Google, Inc.
|Detecting duplicate and near-duplicate files
|3 Sep 1999
|2 Dec 2003
|The Regents Of The University Of California
|User interface for displaying document comparison information
|19 Oct 1999
|30 Dec 2003
|Microsoft Corporation
|System and method for hashing digital images
|27 Jan 1999
|6 Jan 2004
|Kent Ridge Digital Labs
|Digital audio watermarking using content-adaptive, multiple echo hopping
|23 Feb 2001
|3 Feb 2004
|Sony Corporation
|Method for determining a correlation between images using multi-element image descriptors
|23 Mar 1998
|2 Mar 2004
|Fujitsu Limited
|Device for generating, detecting, recording, and reproducing a watermarked moving image having a copy preventing capability and storage medium for storing program or the moving image
|14 Jun 2000
|2 Mar 2004
|International Business Machines Corporation
|Method and apparatus for matching slides in video
|2 Dec 1999
|20 Apr 2004
|Verizon Laboratories Inc.
|Digital watermarking
|20 Sep 1999
|15 Jun 2004
|Ut-Battelle, Llc
|Method for indexing and retrieving manufacturing-specific digital imagery based on image content
|16 Jul 2001
|22 Jun 2004
|Koninklijke Philips Electronics N.V.
|Image retrieval system
|4 Feb 2003
|27 Jul 2004
|Digimarc Corporation
|Digital watermark screening and detection strategies
|6 Jul 2000
|27 Jul 2004
|Thomas W. Meyer
|Method of and apparatus for high-bandwidth steganographic embedding of data in a series of digital signals or measurements such as taken from analog data streams or subsampled and/or transformed digital data
|19 Jan 2000
|27 Jul 2004
|Koninklijke Philips Electronics N.V.
|Invisible encoding of meta-information
|3 Mar 2000
|3 Aug 2004
|Sharp Laboratories Of America, Inc.
|Video skimming system utilizing the vector rank filter
|3 Mar 2000
|24 Aug 2004
|Mcgill University
|Method and apparatus for providing background acoustic noise during a discontinued/reduced rate transmission mode of a voice transmission system
|18 Dec 2000
|28 Sep 2004
|International Business Machines Corporation
|Method and system for generating a characteristic identifier for digital data and for detecting identical digital data
|29 Mar 2000
|4 Jan 2005
|Markany Inc.
|Digital watermarking method and apparatus for audio data
|12 Apr 2002
|8 Mar 2005
|Mitsubishi Electric Research Labs, Inc.
|Analysis, synthesis and control of data signals with temporal textures using a linear dynamic system
|10 Jan 2002
|12 Apr 2005
|Trustees Of Columbia University Of The City Of New York
|Method and apparatus for watermarking images
|1 Jun 2000
|31 May 2005
|Digital Video Express, L.P.
|Secure oblivious watermarking using key-dependent mapping functions
|17 Oct 2000
|14 Jun 2005
|International Business Machines Corporation
|Cryptography-based low distortion robust data authentication system and method therefor
|23 Oct 2002
|15 Nov 2005
|International Business Machines Corp
|Information retrieval system, an information retrieval method, a program for executing information retrieval, and a storage medium wherein a program for executing information retrieval is stored
|15 Dec 2004
|29 Nov 2005
|Microsoft Corporation
|Recognizer of content of digital signals
|24 Apr 2001
|6 Dec 2005
|Microsoft Corp.
|Recognizer of audio-content in digital signals
|17 Jan 2001
|24 Jan 2006
|International Business Machines Corporation
|Methods, systems, and computer program products for securely transforming an audio stream to encoded text
|20 Apr 2001
|24 Jan 2006
|Landmark Digital Services Llc
|System and methods for recognizing sound and music signals in high noise and distortion
|24 Apr 2001
|7 Feb 2006
|Microsoft Corporation
|Robust recognizer of perceptually similar content
|21 Dec 2001
|13 Jun 2006
|Intel Corporation
|Surface light field decomposition using non-negative factorization
|28 Jun 2002
|22 Aug 2006
|Microsoft Corporation
|Watermarking via quantization of statistics of overlapping regions
|12 Feb 2002
|28 Nov 2006
|City University Of Hong Kong
|Sequence generator and method of generating a pseudo random sequence
|4 Nov 2004
|19 Dec 2006
|Microsoft Corporation
|Content-recognition facilitator
|11 Jul 2001
|30 Jan 2007
|Cornell Research Foundation, Inc.
|Method and system for analyzing multi-variate data using canonical decomposition
|4 Nov 2004
|6 Mar 2007
|Microsoft Corporation
|Categorizer of content in digital signals
|22 Dec 2000
|2 Aug 2001
|Wenyu Han
|Method and apparatus for patterning cards, instruments and documents
|19 Dec 2001
|12 Sep 2002
|Brunk Hugh L.
|Method, apparatus and programs for generating and utilizing content signatures
|24 Apr 2001
|21 Nov 2002
|Ramarathnam Venkatesan
|Robust and stealthy video watermarking
|24 Apr 2001
|26 Dec 2002
|Mihcak M. Kivanc
|Robust recognizer of perceptually similar content
|20 Sep 2001
|20 Mar 2003
|Koninklijke Philips Electronics N.V.
|Using multipe watermarks to protect content material
|26 Aug 2002
|22 May 2003
|Ahmed Tewfik
|Digital watermark detecting with weighting functions
|20 Dec 2001
|26 Jun 2003
|Koninklijke Philips Electronics N.V.
|Varying segment sizes to increase security
|11 Mar 2002
|11 Sep 2003
|Nobuo Sasaki
|System and method of optimizing graphics processing
|3 Oct 2001
|9 Oct 2003
|Lidror Troyansky
|Method and system for distributing digital content with embedded message
|3 Mar 2003
|27 Nov 2003
|Rhoads Geoffrey B.
|Digital watermarks
|22 Nov 2002
|27 May 2004
|Radek Grzeszczuk
|Building image-based models by mapping non-linear optmization to streaming architectures
|11 Jul 2001
|20 Jan 2005
|Michael Repucci
|Method and system for analyzing multi-variate data using canonical decomposition
|4 Nov 2004
|24 Mar 2005
|Microsoft Corporation
|Hash value computer of content of digital signals
|4 Nov 2004
|31 Mar 2005
|Microsoft Corporation
|Digital signal watermarker
|22 Nov 2004
|7 Apr 2005
|Microsoft Corporation
|Recognizer of digital signal content
|4 Nov 2004
|21 Apr 2005
|Microsoft Corporation
|Recognizer of content of digital signals
|23 Jan 2004
|28 Jul 2005
|Microsoft Corporation
|Watermarking via quantization of rational statistics of regions
|31 Dec 2001
|18 Aug 2005
|Stmicroelectronics Asia Pacific Pte Ltd
|Video encoding
|29 Sep 1998
|10 Jan 2001
|System and method for authentication of goods
|30 Jul 1993
|2 Feb 1994
|Corbis Corporation
|Method and system for digital image signatures
|19 Mar 2002
|30 Oct 2002
|Microsoft Corporation
|Derivation and quantization of robust non-local characteristics for blind watermarking
|19 Mar 2002
|13 Jul 2005
|Microsoft Corporation
|Derivation and quantization of robust non-local characteristics for blind watermarking
|Title not available
|Title not available
|"Official Notice of Final Decision of Rejection", Japanese Application 2002-123173, Mailed Jul. 4, 2008, 4 pages.
|"The Singular Value Decomposition", at >, Aug. 20, 1999, pp. 3.
|"US ASCII Code Chart", 1968, Wikipedia.
|B. Chen and G. W. Wornell, "Digital watermarking and information embedding using dither modulation," Proc. IEEE Workshop on Multimedia Signal Processing, Redondo Beach, CA, pp. 273-278, Dec. 1998.
|B. Chen and G. W. Wornell, "Dither modulation: a new approach to digital watermarking and information embedding," Proc. of SPIE: Security and Watermarking of Multimedia Contents, vol. 3657, pp. 342-353, 1999.
|B. Chen and G. W. Wornell, "Quantization Index Modulation: A class of Provably Good Methods for Digital Watermarking and Information Embedding," IEEE Trans. Inform. Theory, 1999 and 2000, pp. 1-54.
|Brin, et al., "Copy Detection Mechanisms for Digital Documents", Proceedings of the ACM SIGMOD Annual Conference, San Jose, May 1995, pp. 398-409.
|Chang et al., "A Spatial Match Representation Scheme for Indexing and Querying in Iconic Image Databases", 1997, ACM, Retrieved from the Internet http://portal.acm.org/citation.cfm?id=266890&coll=ACM&dl=ACM&CFID=68519948&CFTOKEN=85922645.
|Chang et al., "RIME: A Replicated Image Detector for the World-Wide Web," Proceedings of the SPIE, vol. 3527, Nov. 2-4, 1998, pp. 58-67.
|Chen et al., "Achievable Performance of Digital Watermarking Systems," IEEE, 1999, pp. 13-18.
|Chen et al., "Quantization Index Modulation Methods for Digital Watermarking and Information Embedding of Multimedia," Journal of VLSI Signal Processing 27, 2001, pp. 7-33.
|Cheng et al. "Aircraft identification based on the algebraic method" Hybrid Image and Signal Processing III, vol. 1702, Apr. 23, 1992, pp 298-305.
|Dittmann, et al., "Content-based Digital Signature for Motion Pictures Authentication and Content-Fragile Watermarking", IEEE 1999, retrieved on Jul. 27, 2008 at , pp. 209-213.
|Echizen et al., "General Quality Maintenance Module for Motion Picture Watermarking," IEEE Trans. on Consumer Electronics, vol. 45 No. 4, Nov. 1999, pp. 1150-1158.
|Echizen, Isao et al., "General Quality Maintenance Module for Motion Picture Watermarking", IEEE Transactions on Consumer Electronics, vol. 45, No. 4, Nov. 1999, 9 pages.
|El-Kwae et al., "Efficient Content-Based Indexing of Large Image Databases", Apr. 2000, ACM, Retrieved from the Internet: http://portal.acm.org/citation.cfm?id=348762&coll=ACM&dl=ACM&CFID=68519948&CFTOKEN=85922645.
|F. A. P. Petitcolas and R. J. Anderson, "Evaluation of copyright marking systems," Proceedings of IEEE Multimedia Systems'99, vol. 1, pp. 574-579, Jun. 7-11, 1999, Florence, Italy.
|Fridrich et al., "Robust Hash Functions for Digital Watermarking," Proc. Int. Conf. on Information Technology: Coding and Computing, Mar. 2000, pp. 178-183.
|I. J. Cox, J. Killian, T. Leighton, and T. Shamoon, "A secure robust watermark for multimedia," Information Hiding Workshop, University of Cambridge, pp. 185-206, 1996.
|J. Dittman, M. Siabenau and R. Steinmetz, "Robust MPEG video watermarking technologies," Proceedings of ACM Multimedia '98, The 5th ACM International Multimedia Conference, Bristol, England, pp. 71-80.
|Kesal et al., "Iteratively Decodable Codes for Watermarking Applications," Proceedings of the Second Symposium on Turbo Codes and Their Applications, France, Sep. 2000, 4 pages.
|Kozat S. S. et al., "Robust Preceptual Image Hashing Via Matrix Invariants" Image Processing, 2004. Oct. 24, 2004.
|Lambrou et al, "Classification of Audio Signals Using Statistical Features on Time and Wavelet Transform Domains", Accoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on Seattle, WA, USA May 12-15, 1998, New York, NY, USA, IEEE, US, vol. 6, pp. 3621-3624.
|Lee et al., "Adaptive Video Watermarking Using Motion Information," Proc SPIE, vol. 3971: Security and Watermarking of Multimedia Contents, Jan. 11, 2000, pp. 209-216.
|Lin et al., "A Robust Image Authentication Method Distinguishing JPEG Compression from Malicious Manipulation", IEEE Transactions, Feb. 2001, vol. 11, No. 2, pp. 153-168.
|Lin, et al., "Generating Robust Digital Signature for Image/Video Authentication", retrieved on Jul. 27, 2008 at >, Multimedia and Security Workshop at ACM Multimedia, '98, Bristol UK, Sep. 1998.
|M. D. Swanson, B. Zhu and B. Chau, "Object based transparent video watermarking," Proceedings of IEEE Signal Processing Society 1997 Workshop on Multimedia Signal Processing, Jun. 23-25, 1997, Princeton, New Jersey, USA.
|M. K. Mihcak and R. Venkatesan, "A tool for robust audio information hiding: A perceptual audio hashing algorithm," submitted to Workshop on Information Hiding, Pittsburgh, PA, 2001.
|Malvar, "A Modulated Complex Lapped Transform and Its Applications to Audio Processing," IEEE ICASSP'99, Phoenix AZ, Mar. 1999, pp. 1-4.
|Mihcak et al., "A Perceptual Audio Hashing Algorithm: A Tool For Robust Audio Identification and Information Hiding," Proceedings of the Information Hiding Workshop, 2001, 15 pages.
|Mihcak et al., "Blind Image Watermarking Via Derivation and Quantization of Robust Semi-Global Statistics," Proceedings of IEEE-ICASSP, Orlando FL, 2002, 4 pages.
|Mihcak et al., "Cryptanalysis of Discrete-Sequence Spread Spectrum Watermarks," Proceedings of the Information Hiding Workshop, Holland 2002, 21 pages.
|Mihcak et al., "New Iterative Geometric Methods for Robust Perceptual Image ashing," Proceedings of the Security and Privacy Digital Rights Management Workshop, 2001, 9 pages.
|Mihcak et al., "Watermarking via Optimization Algorithms for Quantizing Randomized Statistics of Image Regions," Proceedings of the Annual Allerton Conference on Communication Control and Computing, Urbana IL, 2002, 10 pages.
|Mihcak et al.; "Watermarking via optimization algorithms for quantizing randomized semi-global image statistics"; research.microsoft.com/~kivancm/publications/mvl04-rev2.pdf ; Springer-Verlag; Jul. 12, 2005; pp. 1-16.
|Mihcak, et al., "A tool for robust audio information hiding: a perceptual audio hashing algorithm," submitted to Workshop on Information Hiding, Pittsburgh, PA, 2001.
|Moreno et al., "Using the Fisher Kernal Method for Web Audio Classification", Acoustics, Speech , and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on Jun. 5-9, 2000, Piscataway, NJ, USA, IEEE, vol. 6, pp. 2417-2420.
|Moulin et al., "A Framework for Evaluating the Data-Hiding Capacity of Image Sources," IEEE Transactions on Image Processing, vol. 11 No. 9, Sep. 2002, pp. 1-14.
|Moulin et al., "The Parallel-Gaussian Watermarking Game," IEEE Transactions Information Theory, Feb. 2004, pp. 1-36.
|Muramatsu, Taichi et al., "A Sound Database System with Intuitive Retrieval Retrieved by Sound", Proceedings of the 1999 lEICE General Conference, Information System 2, Mar. 8, 2999, SMD-7, pp. 383 and 384.
|Politis et al., "An Audio Signatures Scheme for Dynamic Content Multimedia Databases", Electrotechnical Conference, 2000. IEEE, vol. 2, May 29, 2000, pp. 726-728.
|Pye, D., "Content-Based Methods for the Management of Digital Music", Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on Jun. 5-9, 2000, Piscataway, NJ, USA, IEEE, vol. 6, pp. 2437-2440.
|Schneider et al., "A Robust Content Based Digital Signature for Image Authentication", Proceedings, International Conference, Sep. 1996, vol. 3, pp. 227-230.
|T. Kalker and J. Haltsma, "Efficient detection of a spatial spread-spectrum watermark In MPEG video streams," Proc. IEEE ICIP. Vancouver, Canada, Sep. 2000.
|Takeuchi, Kazuki et al., "Image ID Extraction by Content Analysis and its Applications", Nov. 24, 2000, 8 pages.
|Tsuji, Yasuhiro, et al., "Local Patterns of a Melody and its Applications to Retrieval by Sensitivity Words", lEICE Technical Report, Mar. 6, 1997, vol. 96, No. 565, SP96-124, pp. 17-24.
|Vankatesan et al., "Image Watermarking with Better Resilience," Proceedings of IEEE-ICIP, Vancouver BC Canada, 2000, 4 pages.
|Venkatesan et al., "Robust Image Hashing," Proceedings of the IEEE-ICIP, Vancouver BC Canada, 2000, 3 pages.
|Venkatesan, et al., "Image Hashing", Microsoft Research, Mar. 24, 2000, pp. 1-8.
|Wu et al., "Video Access Control Via Multi-level Data Hiding," Proc. IEEE Int. Conf. on Multimedia and Expo, vol. 1, Jul./Aug. 2000, pp. 381-384.
|Yoshino, Taichi et al., "An Automatic Metadata Creation Method for Musci Data and its Application to Semantic Associative Search", Technical Report of Information Processing Society of Japan, Jul. 10, 1998, vol. 98. No. 58, 98- Dbs-116, pp. 109 to 116.
|Citing Patent
|Filing date
|Publication date
|22 Aug 2012
|18 Nov 2014
|Dolby Laboratories Licensing Corporation
|Audio classification method and system
|U.S. Classification
|713/176, 380/54, 382/251, 380/201
|International Classification
|Cooperative Classification
|European Classification
|18 Mar 2013
|Fee payment
Year of fee payment: 4
|9 Dec 2014
Owner name: MICROSOFT TECHNOLOGY LICENSING, LLC, WASHINGTON
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:MICROSOFT CORPORATION;REEL/FRAME:034543/0001
Effective date: 20141014
|28 Jul 2017
|Maintenance fee reminder mailed


text:
A puppy that was taken from a locked vehicle in Mountain View on Saturday was returned after reports circulated in the media and online, police said.
Uso, a 9-week-old Chiweenie/pit bull mix, was turned in Sunday, Aug. 25, to the police department in good shape, police said via the department's Facebook page.
The dog was taken from a car in the 1300 block of Villa Street sometime between 2:30 p.m. and 2:44 p.m. on Aug. 24, according to Shino Tanaka, social media and community coordinator with the MVPD. Investigators said it appeared that the perpetrator pushed an already cracked window down, reached in and pulled the dog out.
While the return appears to have been triggered by reports in local media and on social media web sites, Tanaka said police aren't sure why the dog was taken in the first place.
Tanaka said that some of the people commenting on the MVPD's Facebook post speculated that the dog may have been taken from the car by someone who felt the animal was being treated cruelly, as temperatures over the weekend reached the mid- to high-70s. However, that was just speculation.
If anyone ever has reason to believe that an animal is being abused, Tanaka said they may anonymously call the MVPD and report it at 650-903-6344.
"Don't take it upon yourself to act," Tanaka said. "We're always happy to respond."
The puppy certainly seemed happy, as did the officers at the station who got a chance to interact with the dog before returning it to its owner on Sunday. "There was a lot of tail wagging and kissing going on in the station," according to the MVPD Facebook post.

subset: commoncrawl


text:
Physical Science
Sort By:
They look like ordinary marbles, but they're magnets too! Build pyramids and other fun shapes, play marble games and more. Each marble is 5/8" in diameter.
500 Metal-Trimmed Counting Chips
Experiment, count and sort with our bright 3/4" round counting chips. They have metal edges so that you can pick them up with a magnet.
A combination of our most popular magnets in one fantastic kit! Includes one each of 1" horseshoe, 2" horseshoe, 2" bar magnet plus tubes of iron filings and steel shapes.
Whether you're launching rings of fog, making a quicksand clock, or defying gravity with anti-gravity beads, you'll experience and engage with fantastic hands-on science in fun and creative ways. Your mind will be tingling with all of the awesome experiments and activities!
More Options...
Classroom Attractions™ Magnet Kit
Supplement your science curriculum with comprehensive magnet kits aligned with the Next Generation Science Standards! Investigate motion and stability, matter and its interactions and more. Designed for groups of 5-10 students. Includes 10 Magnet Wands plus plastic encased button magnets, magnet marbles, ring and disc magnets, bar magnet, metal rimmed counting chips, paper clips, pencils and teacher's guide.
Transparent 6" paddles in six colors with three textures demonstrate the principles of color mixing.
Turn simple stacking into a sensory experience! The blocks stack easily with the help of specially designed grooves. Use the grooves to align the blocks at various angles and create new tower designs. Overlap different block colors to explore color mixing concepts. Use the blocks on a light table to create glowing towers. Includes 50 blocks, 2" x 2-1⁄2" and guide.
Light up your learning with changing colors, strobes, fades and flashes at the touch of a button! Multiple learning surfaces and 16 different colors enhance building, sorting, counting and many art/exploration activities. The innovative light cube is sturdy, safe and extra bright. Chargeable and portable for many hours of indoor and outdoor use. Includes 16" cube, remote control, charger, and teaching guide.
Build an electromagnet, electrical catapult, doorbell, telegraph and more! Includes neodymium and ceramic magnets, LED, switch plates and battery holders, iron filings, compass, wood blocks, hardware, other supplies and detailed activity guide.
Do your friends get a "charge" out of you? Does your body transmit electricity? Use our new Energy Stick to find out! Hold the Energy Stick in your hands and see if it lights up and makes noises. Try it with two people, three people, five or more! A fun way to learn about conductivity, insulators and electric circuits.
Equilateral Prisms, Small, 4" long
Your child will be amazed by the tricks light can play with this equilateral prism.
The perfect starter set for the budding geologist! Each set includes a rock identification chart complete with images and a brief description plus a handy, plastic storage container. Grades 3 and up.


text:
I love Harry Potter books and films and have read the books over and over again. I have read the The Prisoner of Azkaban 26 times in 3 months!
This is an unofficial site and is not supported or recognized by any of the publishers of the Harry Potter books. The author J.K. Rowling created this world and owns all the names and characters in it; this is a humble fan site run by teenagers who are scared of lawyers and don't wish to break any laws. E-mail us at mnffstaff3 at gmail dot com if you have any questions or concerns.


text:
E. Azalia Hackley Collection
The E. Azalia Hackley Collection of African Americans in the Performing Arts was established in 1943 when original materials were presented to the Detroit Public Library by the Detroit Musicians Association to serve as the nucleus for a special black music collection. The first of its kind in the world, the Hackley Collection, named after a Detroit music educator and performer, quickly broadened its scope to include dance, drama and other forms of the performing arts.
Many rare books, manuscripts and archives of performing artists are available to serious researchers. The archives include materials on organizations such as the Motown Recording Company, the National Association of Negro Musicians, on dancers Rael Lamb and Lavinia Williams, singer Roland Hayes and many other concert and opera singers.
The Photographic and Print Collection contains items dating from the mid-19th century to the present, including portraits of performers, organizations, vocal and instrumental groups and still photographs from black films and stage productions. Among the many rare items in the collection are an original photograph of the Fisk Jubilee Singers and a large collection of photographs by Carl van Vechten of prominent Black performers.
Digital reproductions of items in the Hackley Collection may be purchased by completing the Special Collections Reproduction Order Form. Customers are not permitted to use personal cameras or scanners to reproduce photographs or postcards.
The Recorded Sound Collection contains music either performed or composed by Black musicians. Documenting the development of Black music, recordings include African chant, Negro spirituals, ragtime, jazz, gospel, opera, orchestral music, soul and rhythm & blues. Hundreds of examples of "popular music," produced in the middle to late 19th century and based on Negro themes, are similarly preserved in the Sheet Music Collection, along with contemporary "popular music" composed and performed by Black musicians.
Detroit Electronic Music Archive
On June 11, 2005, the Hackley Collection announced the creation of the Detroit Electronic Music Archive. DEMA will document, collection, preserve, and disseminate information about Electronic Music, a music form birthed in the city of Detroit by Detroit musicians.
- E. Azalia Hackley Collection
- Charles H. Wright Museum of African American History
- National Association of Negro Musicians
- National Museum of African American History and Culture
- Schomburg Center for Research in Black Culture
Special Collections Reading Room Guidelines
Donations to Special Collections
Contact Information
E. Azalia Hackley Collection
Detroit Public Library
5201 Woodward Ave.
Detroit, MI 48202
(313) 481-1401
Catalog & Database Search
Catalog Search
Search books, movies, games and other library materials.
Database Search
Search newspaper, magazine and journal articles.


text:
|Rockin' Storytime
Event Type: Story Times
Age Group(s): children
Date: 11/4/2013
Start Time: 9:15 AM
End Time: 10:00 AM
You never knew storytime could be this awesome! Join us weekly on Mondays and Tuesdays at 9:15am as Miss Annie and Miss Melissa rock your storytime with cool books, kickin' kids songs, rad rhymes, and puppets, too! We'll wrap it all up with a themed craft. All ages are welcome.Library: Main Library
Location: Youth Services
Contact: Annie Pendergrass
Contact Number: 256-532-5982


text:
Personal computing discussed
Moderators: Flying Fox, Ryu Connor
I have the exact same issue - I thought I was the only one.
Anyone have any luck with the homegroup option?
Thank you, I'm in the midst of trying your suggestions. However, when I ran the /energy command, the generate report will not come up. IE says File Not Found (Cannot display page). How do I overcome this?
Power Policy:Sleep timeout is disabled (On Battery)
The computer is not configured to automatically sleep after a period of inactivity.
Power Policy:Sleep timeout is disabled (Plugged In)
The computer is not configured to automatically sleep after a period of inactivity.
My computer supports S3, but not S1 or S2, which is normal, right?
Also, I just observed it idle, and it seemed that right when it was supposed to enter sleep, the screen cut back on again. Two errors popped up from MpTelemetry that look like they might have something to do with windows defender - which seem to have happened right when the PC was supposed to go to idle.
Maintenance might help you then. It might have been kicking off a scheduled Defender scan right as the PC was trying to go to sleep. That's just a arrow in the dark at the moment though.
I tried the /request, and found the only thing coming up was the srvnet. I tried to override, yet nothing happened.
Any ideas?
bazb85 wrote:Glad to hear of the success some of us are having with this! : )
I tried the override in the powercfg cmd, and it managed to override the srvnet request that popped up earlier and wouldn't go. But now that it's gone, my PC still won't sleep.
Tried a round of maintenance, still no cigar. I did update to latest drivers, to no avail.
Same thing with shutdown. It successfully restarts, but hangs on shutdown much the same way I close the lid.
Ryu Connor wrote:Good to hear. I've seen Maintenance fix the problem before, so that may have been the silver bullet.


text:
(NNPA)—Bombastic talk show host Rush Limbaugh and Terry Jones, the Gainesville, Fla. pastor who gained worldwide attention by threatening to hold an "International Burn a Koran Day" on Sept. 11, graduated in 1969 from Central High School in Cape Girardeau, Mo.
I visited the school's website to see if I could find any clues as to why this rural city in the Missouri boot heel was an incubator to such peddlers of hate. Many of their classmates appear embarrassed by Jones and, to a less extent, Limbaugh.
The banner headline on the website reads: "Koran-Burning Preacher Terry Jones and Rush Limbaugh: Class of 69." The story says, "Rush Limbaugh USED to be Cape Girardeau's most prominent export. One of his classmates from the Central High School Class of 1969 is dominating the news right now: Terry Jones, the Gainesville, Fla. preacher who is threatening to hold an "International Burn a Koran Day" on Sept. 11.
"In 2010, Jones published "Islam is of the Devil," which denounces Islam as a violent faith.
"His church also maintains a Gainesville boarding school, called the Dove World Outreach Academy. The Gainesville Sun newspaper reported that students of the academy are prohibited from outside and family contact including attendance at family weddings and funerals, and work without compensation selling, packing and shipping furniture for TS and Co., a business owned by his current and second wife, Sylvia. (His first wife was Lisa Barker, of Marble Hill. She died of a heart attack in 1996.)."
Describing Jones as "an equal opportunity hater," the story on the Web continued, "In March 2010, Dove World posted a sign saying 'No Homo Mayor,' referring to Gainesville's first openly gay mayor; after Americans United requested that the Internal Revenue Service investigate the sign as an undue participation of a non-political tax-exempt organization in the political process, the church then changed the sign to simply read 'No Homo.'
"On April 18, 2010, members of Dove World participated in a joint protest against homosexuality with the Westboro Baptist Church, a group known for disrupting the funerals of U.S. soldiers. On April 21, Dove World member Fran Ingram published a blog post proclaiming the church's endorsements of the Westboro Baptist Church's protests against homosexuality and homosexuals."
The "equal opportunity hater" brought liberals and conservatives together, to condemn his plans. In the end, Jones cancelled his divisive act of defiance, but by then, the damage had been done, setting off riots in some predominantly Muslim countries.
How is Terry Jones playing in Cape Girardeau? A former classmate, Judy Temple, asked the Number One question on my mind: What WERE those two smokin' back then?"
Someone identified as "Ismellarat," said, "Rush has talked about Jones on his show and did not mention the connection…I think we should dig deeper into the relationship between these two. Rush stirs us up, makes fools of us, stands back as we come to blows, and then laughs all the way to the bank."
Linda Strange wrote, "I lived in Cape until 1965 and attended Jefferson School from the second through the sixth grade. Terry Jones was in my class. All I can remember about him is that he had difficulty reading and was in remedial classes for it. Can't recall his ever saying much of anything, but he was labeled 'dumb' by many of the kids. Maybe that's what made him so full of hate."
Jones had his supporters, though they were in the distinct minority.
Wayne Boswell wrote, "I had no idea that Terry Jones was from Cape, but I was really happy to find out he was. Since he has attracted national attention, maybe even worldwide attention with his threat to burn a Koran, I would like to encourage him to form a coalition of religious leaders from the United States or maybe even worldwide to petition the leaders of the Muslim Church to change their doctrines about 'killing infidels' and Sharia Law. If they will not agree to do this, then it will at least expose them for what they really are."
Lori Robinson Smith, who described herself as slightly to the right of Attila the Hun, observed, "I don't agree with Terry Jones in burning the Quran, only because it would do nothing but incite Muslims to violence. Is it within his right to do it? Yes, he has the First Amendment right to do it. Is it insensitive? Absolutely. I would not like it if someone burned the Bible. I wouldn't riot and kill over it, but I would be insulted."
Another classmate, Gregg Hopkins, said, "I knew him in the early '70s. He graduated from Central (I think) in '69. He was a funny, friendly guy back then, when he was dating my friend, Lisa. My, how the years change some people. Every picture I've seen of him, he's wearing an intense scowl. A couple of our Marble Hill friends figured out his connection about the same time I did. Sickening, his former in-laws, Lisa's parents, are fine folks. I feel embarrassed for them."
(George E. Curry, former editor-in-chief of Emerge magazine and the NNPA News Service, is a keynote speaker, moderator and media coach. He can be reached through his website, http://www.georgecurry.com Follow him at http://www.twitter.com/currygeorge.)


text:
Periodically, our employees volunteer for charitable activities and coordinate fundraising events to support local organizations. These are creative, fun-focused affairs and parties with a purpose that always draw a crowd. As a Community Gives participant, you're always invited!
Here's a look at some of our past fundraising events and initiatives:
Bux Back partnering with Susan G. Komen for the Cure Fundraiser: The San Diego Affiliate of Susan G. Komen for the Cure is dedicated to combating breast cancer at every front. This event gave guests several fun ways to raise money for the foundation, including a mermaid in the pool!
Vintage Prom: This event, held in May 2010 and 2011, was a benefit for the San Diego Brain Tumor Foundation. Guests dressed to the nines and danced the night away for this special themed celebration—a throwback from everyone's youth, featuring music from the 50's, 60's, 70's, 80's and 90's. In 2011 this event raised approximately $3,500.00
Ping Pong Social: This annual event is held in April each year and our 5th Annual event this year raised over $12,000.00. It includes the fun of a ping pong tournament, poolside social and silent auction. All proceeds from the silent auction and tournament went directly to the School of Business at San Diego High School, to help its hard-working students fund their senior trip to New York City.
Community Water
Water is essential to our lives, and to our planet.
At Eat at the Pearl, we encourage the use of pure, sustainable products. We invite you to enjoy our naturally purified water from nearby Palomar Mountain, bottled in our own Community brand sustainable glass bottles.
$1.00 from each sale is donated to a clean water program in Ghana, Project MIM.
Community is a sister brand of The Pearl Hotel and strives to knock down barriers and support local efforts to create a stronger sense of civic unity.
Happy hydrating!
Community Gives
The mission statement of the Community brand of properties states, "We seek to participate in the traditions of and enhance the quality of life in the communities where we invest." This desire to serve and give back to San Diego, inspired us to create Community Gives, a monthly, $1.00 opt-in, charitable giving program open to Community's residential and commercial tenants, as well as to patrons of L.W.P. Group hospitality ventures.
Your $1.00 contribution and those of all participants will be collected monthly and matched in full by L.W.P. Group, Inc. All donations, and our matching contribution, will support San Diego-area organizations focused on:
- Youth and education
- Health and wellness
- Environmental sustainability
- Smart growth and development
These types of organizations highlight areas crucial to the growth of great, modern communities. They inspire us on a daily basis, and Community Gives represents our way of acknowledging and furthering their good work. We hope you will join us in this exciting endeavor.
ARTS is in our hearts and everywhere at The Pearl, for that matter. Each of our guest rooms features a framed piece of art created by a child involved in ARTS – A Reason to Survive! ARTS is a San Diego-based non-profit organization dedicated to bringing hope, healing, and self confidence to the lives of children and youth facing adversities by providing expressive opportunities through the visual, performing and literary arts.
ARTS is entirely dependent on donations for its operation and maintenance. The Pearl Hotel supports ARTS, and encourages you, our guests, to embrace this worthy cause and experience the same joy and reward. You can assist ARTS by:
- Donate directly via the official ARTS website


text:
Oscar Predictions Video 2013
Video: The Oscars' Toughest Matchups: Who Will Win?
There are a few actors who seem like shoo-ins for Oscars this year, but there are still some big categories that have us wondering. Who will win best actress? Best supporting actor? Or the big one, best picture? Watch as our editors weigh in with their picks for this year's biggest Oscar matchups in this episode of The Buzz.


text:
USTA to Fund Research into Cobalt Use
- Jun 17, 2014
- Topics:
The United States Trotting Association (USTA) said June 16 it will fund a project designed to develop regulatory controls for the use of cobalt in racehorses.
Cobalt is used to stimulate red blood cell production, and USTA officials say the substance is being detected in all breeds of racehorses.
The research will be performed by George Maylin, DVM, PhD, of the New York Drug Testing and Research Program at Morrisville State College in New York. He will be assisted by Karyn Malinowski, PhD, and Ken McKeever, MS, PhD, FACSM, the director and associate director, respectively, of the Equine Science Center at Rutgers University in New Jersey.
"It has become obvious that in all racing breeds the presence of cobalt is being detected," USTA president Phil Langley said in a release. "Since it is a naturally occurring substance, the question that arises is when the amount is natural and when is it added to a horse's system to enhance performance. We believe this research will give all breeds a standard that will withstand any court challenges."
Maylin said he initiated discussions with the USTA and Joe Faraldo, president of the Standardbred Owners of New York and a USTA director.
"Cobalt has been rumored to be used with horses for a number of years," Maylin said. "It started perhaps six or seven years ago. So we started delving into the literature and did research. It became apparent that cobalt was getting widespread use, but only recently has there been concern in horses."
Maylin said the research requires new testing equipment. "We now have a state-of-the-art instrument, the Agilent 8800 Triple Quadrupole Mass Spectrometer ICP-MS, that we are going to get access to lease for five years and have to a way to pay for it, thanks to the USTA."
Maylin said he hopes the results of the research are available by the end of this summer.
Originally published on BloodHorse.com.
About the Author
The Blood-Horse Staff
The Blood-Horse is the leading weekly publication devoted to international Thoroughbred racing and breeding. Since 1916, the staff of The Blood-Horse has served the Thoroughbred community with the highest standards of journalistic excellence to provide comprehensive and timely editorial coverage and analysis.
Related Content
Using Vital Signs to Monitor Racehorses
Feeding and Fueling Racehorses
AAEP On Call Veterinarian Case Examples
Bandaging Racehorses' Legs
Kentucky Derby: On Call Vets' Familiarity With Contenders
Medications in Horse Racing
AAEP On Call Program at the Kentucky Derby
Shoeing Racehorses
Electrolyte Use in Performance Horses
Regulatory Veterinarian Checks in Horse Racing