Secure and robust sift with resistance to chosen-plaintext attack
Scale-invariant feature transform (SIFT) is a powerful tool extensively used in the community of pattern recognition and computer vision. The security issue of SIFT, however, is relatively unexplored. We point out the potential weakness of SIFT, meaning that the SIFT features can be deleted or destroyed while maintaining acceptable visual qualities. To properly achieve the tradeoff between security and robustness of SIFT, we present a cube-based secure transformation mechanism to enable the SIFT method to resist up to the chosen plaintext attack while robustness against geometric attacks can still be maintained. Security analysis and robustness verification are provided to demonstrate the effectiveness of the proposed (and modified) SIFT method.