Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Effective and scalable video copy detection
Proceedings of the international conference on Multimedia information retrieval
Understanding the security and robustness of SIFT
Proceedings of the international conference on Multimedia
Vlfeat: an open and portable library of computer vision algorithms
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
Image Signature: Highlighting Sparse Salient Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The performance of Content-Based Image Retrieval Systems (CBIRS) is typically evaluated via benchmarking their capacity to match images despite various generic distortions such as crops, rescalings or Picture in Picture (PiP) attacks, which are the most challenging. Distortions are made in a very generic manner, by applying a set of transformations that are completely independent from the systems later performing recognition tasks. Recently, studies have shown that exploiting the finest details of the various techniques used in a CBIRS offers the opportunity to create distortions that dramatically reduce the recognition performance. Such a security perspective is taken in this paper. Instead of creating generic PiP distortions, it proposes a creation scheme able to delude the recognition capabilities of a CBIRS that is representative of state of the art techniques as it relies on SIFT, high-dimensional k-nearest neighbors searches and geometrical robustification steps. Experiments using 100,000 real-world images confirm the effectiveness of these security-oriented PiP visual modifications.