A New Distance Measurement Method for Perceptual Image Hashing

Abstract

A large number of perceptual image hashing schemes have been developed in recent decades, and they usually calculated the hash distances using Euclidean distance, correlation coefficient, normalized Hamming distance, etc. These hash distances are not customized measurements for hash sequences, which are all single values. To this end, a new distance measurement method, i.e. a distance vector instead of a single value, is proposed in this paper. Since a hash sequence can be regarded as the compressed representation of image perceptual content, the correlation between images is maintained between hash sequences. In our method, the correlation between hash sequences is captured by residuals and co-occurrence matrix to form a distance vector. The correlation differences between perceptually similar and distinct images can be captured in the obtained distance vectors. Using a binary classifier, it is easy to classify the distance vectors of perceptually similar image pairs and perceptually distinct image pairs. As a result, content authentication accuracy of perceptual image hashing can be effectively increased, which is verified by experimental results.