{'fraction_non_alphanumeric': 0.024992040751353074, 'fraction_numerical': 0.009073543457497613, 'mean_word_length': 5.39816700610998, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Image Recapture Detection with Convolutional and Recurrent Neural Networks

In this paper, we aim to address the image recapturing detection problem with the convolutional and recurrent neural networks. With the advances of image display and acquisition techniques, the recaptured images are of satisfactory quality nowadays. This has been creating an ever stronger demand for sophisticated image recapturing detection algorithms which can efficiently prevent the unauthorized image distributing and forging. In this paper, we propose a hierarchical feature learning strategy by leveraging the intra-block information and inter-block dependency for image recapturing detection. In particular, the image blocks are first employed as the input of the convolutional neural network (CNN) and subsequently recurrent neural network (RNN) is further adopted to extract block dependencies. The CNN and RNN serve as effective tools to extract discriminative and meaningful features regarding both intraand inter-block information. As such, the inherent properties within local blocks and the correlations between non-local neighbouring blocks are all exploited to identify the recaptured images. Experimental results on three databases show that significantly better performance can be achieved with our proposed framework compared to traditional handcrafted and deep learning based approaches. Introduction With the technological advances of digital image acquisition and display, it becomes effortless to recapture an authorized image from printed paper and LCD monitor screen under wellcontrolled conditions. In general, image recapturing brings more authenticity to fool people. According to the study in [1], human beings are difficult in distinguishing the original and recaptured images. A similar problem, face spoofing, which aims at fooling the face recognition system using fake facial images/videos, can also be regarded as the recapturing problem. It refers to the attack by presenting a printed face on paper or replaying a video by mobile devices (e.g. mobile phone or tablet) to get through the face recognition system. In [2], it is reported that the state-of-the-art Commercial Off-The-Shelf face recognition systems are even vulnerable to face spoofing attack. As a result, there is considerable concern regarding the security issue of biometric identification. The multimedia reproduction has inspired numerous algorithms proposed to address the problem of image recapturing and face anti-spoofing detection to prevent the illegal use of recaptured images. One of the earliest works was reported by Farid and Lyu [5], who used wavelet statistical features to detect image scanning. It achieves an average accuracy of 99.6% using their proposed dataset. The physical based methods [7], such as the spatial distribution of specularity and chromaticity features have also been used to distinguish between the original image and the printed version. The detection of recaptured image from LCD screen was first proposed by Cao and Kot [1]. The texture pattern caused by aliasing was detected by calculating multiple scales of Local Binary Pattern (LBP) feature, which has been proved to be very powerful in texture classification. The loss of detail, which is caused by the relatively low resolution of LCD screen, is detected by computing multi-scales wavelet decomposition where the absolute mean and standard values of Haar wavelet coefficients are used as features. Since the texture and physical artifacts may also influence the frequency spectrum of images, Yin and Fang [4] proposed to use DCT based Markov Transition Matrix to model DCT coefficients dependencies under the assumption that they may get tampered by recapturing. A recent work [10] proposed a capturing process which can get rid of the unexpected texture pattern. In particular, images captured under the specified conditions showed better quality compared with normal recapture images. On the other hand, they proposed to employ blur degree as discriminative features for image recapturing detection. More recently, a convolutional neural network (CNN) network with Laplacian filter was proposed in [9] to learn the deep representation of recapturing detection which shows promising performance even based on small image patches. Computer Graphic Image (CGI) Detection shares a similar idea with image recapturing detection. CGI detection aims at distinguishing realistic photo with computer generated photo. A multi-scale detection approach was proposed in [11] where local fractal dimension and local patches were used as the finest scale feature. In addition, Beltrami flow vectors were adopted as the intermediate scale feature. In view of the fact that CGI detection and image recapturing detection are different to some extent since computer generated photo can still be considered as originally captured photo, Gao et al. [7] showed that the proposed feature in [11] can be transferred to image recapturing detection. Another similar topic is steganalysis which targets on detecting hidden message inside a given image. As stated in [7], image recapturing detection problem is similar to steganalylsis by considering both of them as recognition problem. In particular, they can be regarded as detecting the variations on statistical properties. In [12], the Markov based Transition Probability matrix is computed as the feature to detect both intra-block and inter-block statistics change for steganalysis. In this paper, we target at learning the powerful deep representation for image recapturing detection which can extract both intra-block and inter-block information. Our motivation originates from two observations. Firstly, recapturing process introduces unexpected artifacts, which can be further categorized into three different parts including loss-of-the-detail artifact, color distortion and anti-aliasing texture [1]. As such, we expect to learn an appropriate representation with CNN network to account for these artifacts. Secondly, natural images hold a certain interIS&T International Symposium on Electronic Imaging 2017 Media Watermarking, Security, and Forensics 2017 87 https://doi.org/10.2352/ISSN.2470-1173.2017.7.MWSF-329 © 2017, Society for Imaging Science and Technology