Image hashing has attracted much attention of the community of multimedia security in the past years. It has been successfully used in social event detection, image authentication, copy detection, image quality assessment, and so on. This paper presents a novel image hashing with low-rank representation (LRR) and ring partition. The proposed hashing finds the saliency map by the spectral residual model and exploits it to construct the visual representation of the preprocessed image. Next, the proposed hashing calculates the low-rank recovery of the visual representation by LRR and extracts the rotation-invariant hash from the low-rank recovery by ring partition. Hash similarity is finally determined by L2 norm. Extensive experiments are done to validate effectiveness of the proposed hashing. The results demonstrate that the proposed hashing can reach a good balance between robustness and discrimination and is superior to some state-of-the-art hashing algorithms in terms of the area under the receiver operating characteristic curve.
Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP
