Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
Efficient Image Matching with Distributions of Local Invariant Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Detection of near-duplicate images for web search
Proceedings of the 6th ACM international conference on Image and video retrieval
Near-duplicate keyframe retrieval by nonrigid image matching
MM '08 Proceedings of the 16th 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
Security-oriented picture-in-picture visual modifications
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Proceedings of the 20th ACM international conference on Multimedia
SIFT keypoint removal and injection for countering matching-based image forensics
Proceedings of the first ACM workshop on Information hiding and multimedia security
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Scale-invariant feature transform (SIFT) is a powerful tool extensively used in the community of pattern recognition and computer vision. However, the security issue of SIFT is relatively unexplored in the literature. This paper investigates the potential weakness of SIFT, meaning that the SIFT features can be deleted or destroyed while maintaining acceptable visual qualities. We then propose an improved scheme to enhance the security of SIFT by introducing a key-based transform process to images. Experimental results demonstrate the effectiveness of our methods.