Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contrast limited adaptive histogram equalization
Graphics gems IV
Coherence-Enhancing Diffusion Filtering
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Understanding the security and robustness of SIFT
Proceedings of the international conference on Multimedia
Deluding image recognition in sift-based cbir systems
Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
Region duplication detection using image feature matching
IEEE Transactions on Information Forensics and Security
Brightness preserving dynamic fuzzy histogram equalization
IEEE Transactions on Consumer Electronics
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery
IEEE Transactions on Information Forensics and Security - Part 2
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Scale Invariant Feature Transform (SIFT) has been widely employed in several image application domains, including Image Forensics (e.g. detection of copy-move forgery or near duplicates). Until now, the research community has focused on studying the robustness of SIFT against legitimate image processing, but rarely concerned itself with the problem of SIFT security against malicious procedures. Recently, a number of methods allowing to remove SIFT keypoints from an original image have been devised. Although quite effective, such methods produce an attacked image with very few (or no) keypoints, thus leaving cues that can be easily exploited by a forensic analyst to reveal the occurred manipulation. In this paper, we explore the topic of reintroducing fake SIFT keypoints into a previously cleaned image in order to address the main weakness of the existing removal attacks. In particular, we evaluate the fitness of locally adaptive contrast enhancement methods to the task of injecting new keypoints. The results we obtained are encouraging: (i) it is possible to effectively introduce new keypoints whose descriptors do not match with those of the original image, thus concealing the removal forgery; (ii) the perceptual quality of the image following the removal and injection attacks is comparable to the one of the original image.