Secure and robust SIFT
Chao-Yung Hsu, Chun-Shien Lu, Soo‐Chang Pei · 2009
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.