An approach to identification of synthetic images is based partly on the concept that selected statistical properties of pattern noise in images of real scenes acquired by digital cameras can be expected to differ from the corresponding statistical properties of pattern noise in synthetic images. These statistical properties, along with artifacts of demosaicking and image-quality metrics, are used as features to be processed by a classification algorithm. In tests of this approach on real and synthetic images, synthetic images were identified with an average accuracy of 93 percent.

•Detection of Forgery or Alteration via Variations in Image Features

In this approach, one designates a set of features that are sensitive to image tampering and determines the ground truth for these features by analysis of original (unaltered) and tampered images. Subsequently, tampering is detected on the basis of the deviation of its measured features from the ground truth.

•Detection of Forgery or Alteration via Inconsistences in Image Features

Image tampering often involves local adjustments of sharpness versus blurriness. Hence, the blurriness characteristics in tampered parts are expected to differ from those in non-tampered parts. Therefore, one approach to detection of tampering involves the use of regularity properties of wavelet-transform coefficients to identify local variations in sharpness and blurriness of edges, which variations could be indicative of tampering.

This work was done by Nasir Memon and Husrev T. Sencar of Polytechnic University, Brooklyn, for the Air Force Research Laboratory.

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