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AN INTEGRATED TECHNIQUE DCT & SIFT FOR DETECTION OF IMAGE FORGERY

A digital image is the most genuine way to spread the message nowadays. An image can be termed as forged based upon the context in which it is used. The other side of forgery is those who perpetuate a forgery for some gain, popularity or prestige. They create an image in which to fool the recipient making them believe that the image is real and from this they are able to gain payment and fame. Detecting such types of forgeries has become a severe problem at present. To determine whether a digital image is original or doctored is a challenging task. To find the marks of tampering in a digital image is a challenging task. Now these marks of tampering or forgery can be done by various operations or methods such as rotation, scaling, JPEG compression, Gaussian noise etc. known as attacks. There are various methods proposed in this field in recent years to detect above mentioned attacks. Also different approaches and methodologies are used to detect image forgery. Here in this paper we have proposed to detect combination of different post-processing operations by a single method. It is analyzed that block-based feature technique DCT is robust to Gaussian noise and JPEG compression and that the keypoint-based feature technique SIFT is robust to rotation and scaling. This research is been carried out in Matlab and have been implemented successfully by overcoming the drawbacks of both the algorithms DCT and SIFT. Thus by combining SIFT and DCT we are able to detect forgery under post-processing operations of rotation, scaling, Gaussian noise, and JPEG compression and thus the efficiency to detect forgery improves by this integrated technique.