Perceptual image hashing based on three-dimensional global features and image energy

Abstract

In order to improve classification performance and operating efficiency of the hash algorithm, this paper proposes a novel hash algorithm that combines three-dimensional global features and local energy features. During the stage of three-dimensional features extraction, the image is firstly compressed by SVD decomposition to form a secondary image. Then the statistical features of the secondary image at the three-dimensional visual angle are extracted as the global features. Finally, the global feature hash is generated by using the relationship between the statistical features of the image layers from different three-dimensional visual angles. In the energy feature extraction stage, the luminance image is divided into blocks, and then the energy value of each image sub-block is obtained. The multi-directional energy change features are taken as the local features of the image. Subsequent experimental results prove the effectiveness of the proposed algorithm. The algorithm not only has good robustness to the conventional content-preserving operations, but also achieves a good balance between discrimination capability and robustness. In addition, compared with several state-of-the-art schemes, this algorithm has the best ROC curve, the shortest running time and the best local tamper detection ability.