A numerically stable fragile watermarking scheme for authenticating 3D models
Computer-Aided Design
Image copy detection using a robust gabor texture descriptor
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Web-Traveler Policies for Images on Social Networks
World Wide Web
Scale-rotation invariant pattern entropy for keypoint-based near-duplicate detection
IEEE Transactions on Image Processing
Robust image copy detection using multi-resolution histogram
Proceedings of the international conference on Multimedia information retrieval
Affine Stable Characteristic based sample expansion for object detection
Proceedings of the ACM International Conference on Image and Video Retrieval
A novel image copy detection scheme based on the local multi-resolution histogram descriptor
Multimedia Tools and Applications
Multimedia Tools and Applications
Content distribution and copyright authentication based on combined indexing and watermarking
Multimedia Tools and Applications
Sparsity cue in image copy detection
Proceedings of the 20th ACM international conference on Multimedia
An efficient expanding block algorithm for image copy-move forgery detection
Information Sciences: an International Journal
Saliency-Based region log covariance feature for image copy detection
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
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Conventional image copy detection research concentrates on finding features that are robust enough to resist various kinds of image attacks. However, finding a globally effective feature is difficult and, in many cases, domain dependent. Instead of simply extracting features from copyrighted images directly, we propose a new framework called the extended feature set for detecting copies of images. In our approach, virtual prior attacks are applied to copyrighted images to generate novel features, which serve as training data. The copy-detection problem can be solved by learning classifiers from the training data, thus, generated. Our approach can be integrated into existing copy detectors to further improve their performance. Experiment results demonstrate that the proposed approach can substantially enhance the accuracy of copy detection.