Perceptual Image Hashing Research Articles

Structural Feature-Based Image Hashing and Similarity Metric for Tampering Detection

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. (This work was supported by the NSF of China (60773079, 60872116, and 60832010), the High-Tech Res. and Dev. Prog. of China (2007AA01Z477), the Innovative Res. Fdn. of Shanghai Univ. for Ph.D. Programs (shucx080148), and the Sci. Res. Fdn. of Guangxi Normal Univ. for Doctors.)

Multipurpose Perceptual Image Hashing Based on Block Truncation Coding

Multipurpose Perceptual Image Hashing Based on Block Truncation Coding

A Clustering Based Approach to Perceptual Image Hashing

A perceptual image hash function maps an image to a short binary string based on an image's appearance to the human eye. Perceptual image hashing is useful in image databases, watermarking, and authentication. In this paper, we decouple image hashing into feature extraction (intermediate hash) followed by data clustering (final hash). For any perceptually significant feature extractor, we propose a polynomial-time heuristic clustering algorithm that automatically determines the final hash length needed to satisfy a specified distortion. We prove that the decision version of our clustering problem is NP complete. Based on the proposed algorithm, we develop two variations to facilitate perceptual robustness versus fragility tradeoffs. We validate the perceptual significance of our hash by testing under Stirmark attacks. Finally, we develop randomized clustering algorithms for the purposes of secure image hashing.