Robust image hashing based on low-rank and sparse decomposition

We propose in this paper a low-rank and sparse decomposition based image hashing algorithm, aiming to summarize the structural information and sparse salient components of digital image to compact digest. More specifically, we leverage compressive sampling and random projection to separately aggregate the low-rank approximation of input image and the spatial layout of salient components into binary hash. Owing to its capability of capturing and fusing intrinsic visual characteristics, the proposed work demonstrates high robustness and discriminability. As observed in content identification experiments, it shows much higher accuracy than state-of-the-art algorithms. Furthermore, we also analytically evaluate the security of the proposed hashing algorithm using the entropy based metric, and its performance in content identification is analyzed using the channel coding theorem.