Image Hash Minimization for Tamper Detection

Tamper detection using image hash is a very common problem of modern days. Several research and advancements have already been done to address this problem. However, most of the existing methods lack the accuracy of tamper detection when the tampered area is low, as well as requiring long image hashes. In this paper, we propose a novel method objectively to minimize the hash length while enhancing the performance at low tampered area.Comment: Published at the 9th International Conference on Advances in Pattern Recognition, 201

Robust hashing for image authentication using SIFT feature and quaternion Zernike moments

International audienceA novel robust image hashing scheme based on quaternion Zernike moments (QZMs) and the scale invariant feature transform (SIFT) is proposed for image authentication. The proposed method can locate tampered region and detect the nature of the modification, including object insertion, removal, replacement, copy-move and cut-to-paste operations. QZMs considered as global features are used for image authentication while SIFT key-point features provide image forgery localization and classification. Proposed approach performance were evaluated on the color images database of UCID and compared with several recent and efficient methods. These experiments show that the proposed scheme provides a short hash length that is robust to most common image content-preserving manipulations like large angle rotations, and allows us to correctly locating forged image regions as well as detecting types of forgery image