Image hashing is an emerging technology for the need of, such as image authentication, digital watermarking, image copy detection and image indexing in multimedia processing, which derives a content-based compact representation, called image hash, from an input image. In this paper we study a robust image hashing algorithm with histogram of color vector angles. Specifically, the input image is first converted to a normalized image by interpolation and low-pass filtering. Color vector angles are then calculated. Thirdly, the histogram is extracted for those angles in the inscribed circle of the normalized image. Finally, the histogram is compressed to form a compact hash. We conduct experiments for evaluating the proposed hashing, and show that the proposed hashing is robust against normal digital operations, such as JPEG compression, watermarking embedding, scaling, rotation, brightness adjustment, contrast adjustment, gamma correction, and Gaussian low-pass filtering. Receiver operating characteristics (ROC) curve comparisons indicate that our hashing performs much better than three representative methods in classification between perceptual robustness and discriminative capability.
['Zhenjun Tang', 'Yumin Dai', 'Xianquan Zhang', 'Shichao Zhang']
Perceptual image hashing with histogram of color vector angles