Image Perceptual Hashing for Content Authentication Based on Geometric Invariant Vector Distance

Abstract

Image perceptual hashing is broadly applied in image content authentication, recognition, retrieval, and social media hotspot event detection. An image authentication algorithm is put forward based on the Itti visual saliency model and geometric invariant vector distance. To begin with, the image is preprocessed and weighted by the Itti model and contourlet transform. After that, the weighted image is randomly divided into blocks, and the image feature vector is constructed by calculating the geometric invariant vector distance on both Hu invariant moment vector and maximum singular value vector of the random blocks. In the end, the feature vector is quantized and encrypted to generate the ultimate hash. Experimental results illustrate that when the threshold T = 70, the true positive rate $P_{\mathrm{T}\mathrm{P}\mathrm{R}}$ for duplicate images stands at 0.96574, while the false rate $P_{\mathrm{F}\mathrm{P}\mathrm{R}}$ of different images is merely 0.0224, with the total error rate reaching the minimum value (0.0566). Furthermore, the AUC value of the proposed algorithm is 0.9951, which is higher than that of the comparison algorithms, indicating that the algorithm has better performance than other state-of-the-art algorithms in terms of various visual content-preserving attacks.