Affiliations: School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China. tangzj230@163.com; shuowang@shu.edu.cn, xzhang@shu.edu.cn, wwm@shu.edu.cn
Note: [] This work was supported by the Natural Science Foundation of China (60773079, 60872116, and 60832010), the High-Tech Research and Development Program of China (2007AA01Z477), the Innovative Research Foundation of Shanghai University for Ph.D. Programs (shucx080148), and the Scientific Research Foundation of Guangxi Normal University for Doctors. The authors would like to thank the anonymous referees for their valuable comments and suggestions. Also works: Department of Computer Science, Guangxi Normal University, Guilin 541004, China
Note: [] Address for correspondence: School of Communication and Information Engineering, Shanghai University, 149 Yanchang Road, Shanghai 200072, China.
Abstract: Structural image features are exploited to construct perceptual image hashes in this work. The image is first preprocessed and divided into overlapped blocks. Correlation between each image block and a reference pattern is calculated. The intermediate hash is obtained from the correlation coefficients. These coefficients are finally mapped to the interval [0, 100], and scrambled to generate the hash sequence. A key component of the hashing method is a specially defined similarity metric to measure the “distance” between hashes. This similarity metric is sensitive to visually unacceptable alterations in small regions of the image, enabling the detection of small area tampering in the image. The hash is robust against content-preserving processing such as JPEG compression, moderate noise contamination, watermark embedding, re-scaling, brightness and contrast adjustment, and low-pass filtering. It has very low collision probability. Experiments are conducted to show performance of the proposed method.
