Image forensic signature for content authenticity analysis

A novel image forensic approach for content authenticity analysis is proposed. We call it forensic signature. It is a compact and scalable representation generated by proper selecting robust features from the original image. In the proposed method, adaptive Harris corner detection algorithm is used to extract image feature points, then the statistics of feature point neighborhood are used to construct forensic signature. This forensic signature can provide evidence for analyzing the processed history of the received image at a lower computational cost, including geometric transform estimation, tampering detection and tampering localization. The characteristics of the proposed method are: (1) It provides a novel forensics analysis tool for tracing the processed history of the image. (2) It achieves a trade-off between robustness against content-preserving manipulations and sensitivity for the changes caused by malicious attacks. (3) By using Fisher criterion, it provides an adaptive method to generate the signature matching threshold value. (4) It can detect subtle changes in texture and color. Experimental results show that proposed method is robust for content-preserving manipulations such as JPEG compression, adding noise, and filtering, etc., and it is also capable to trace the processed history of the received image.