Robust image hashing using ring-based entropies

Image hashing is an emerging technology in multimedia security for applications such as image authentication, digital watermarking, and image copy detection. In this paper, we propose a robust image hashing based on the observations that image pixels of each ring are almost unchanged after rotation and ring-based image entropies are approximately linearly changed by content-preserving operations. This hashing is achieved by converting the input image into a normalized image, dividing the normalized image into different rings and extracting the ring-based entropies to produce hash. Hash similarity is measured by correlation coefficient. Experiments show that our hashing is robust against content-preserving manipulations such as JPEG compression, watermark embedding, scaling, rotation, brightness and contrast adjustment, gamma correction and Gaussian low-pass filtering. Receiver operating characteristics (ROC) curve comparisons with notable algorithms indicate that our hashing has better classification performances than the compared algorithms.

► We find that image pixels in each ring are almost unchanged after image rotation. ► We also find that ring-based image entropies are approximately linearly changed. ► We propose a robust image hashing based on the above observations. ► Our hashing is robust against content-preserving manipulations such as rotation. ► Our hashing has better classification performances than some notable algorithms.