Perceptual image hashing with selective sampling for salient structure features

In this paper, a robust and secure image hashing scheme based on salient structure features is proposed, which can be applied in image authentication and retrieval. In order to acquire the fixed length of image hash, the pre-processing for image regularization is first conducted on input image. Salient edge detection is then applied on the secondary image, and a series of non-overlapping blocks containing the richest structural information in the secondary image are selectively sampled according to the edge binary map. Dominant DCT coefficients of the sampled blocks with their corresponding position information are retrieved as the robust features. After the compression with dimensionality reduction for the concatenated features, the final hash can be produced. Experimental results show that the proposed scheme has better performances of perceptual robustness and discrimination compared with some state-of-the-art schemes.