A novel image hashing with visual and structural feature maps for content authentication

Content authentication is a significant problem of the research of image security. This paper exploits visual and structural feature maps to construct a new image hashing algorithm for content authentication. The visual feature map is extracted by the luminance contrast model. Since the visual feature map can reflects image regions of human visual attention, the features of the tampered areas are captured in the hash. In addition, the structural feature map is extracted by the dual-tree complex wavelet transform. Next, the block krylov singular value decomposition is applied to the visual feature map and the structural feature map for constructing feature vectors. Finally, feature vector distances are calculated and encrypted to produce hash. Extensive experiments are performed on the public databases to demonstrate effectiveness. The results demonstrate that the proposed algorithm can correctly detect 89.83% similar images and 93.93% forged images under the optimal threshold. The comparative results show that the proposed algorithm outperforms some baseline algorithms in content authentication.