Effective Image Hashing With Deep and Moment Features for Content Authentication

Zixuan Yu, Lv Chen, Xiaoping Liang, Xianquan Zhang, Zhenjun Tang

Abstract: Hashing is an efficient technology for various image tasks. This article proposes an effective image hashing with deep and moment features for content authentication. The deep features are calculated by Wavelet scattering network (ScatNet) and local tangent space alignment (LTSA). The ScatNet is used to construct a third-order tensor from the image brightness component in the polar coordinates transformation (PCT) domain and the LTSA is used to learn the compact features from the third-order tensor. The moment features are contributed by the tchebichef moments (TMs) and quaternion bessel fourier moments (QBFMs), where the TMs can measure shape features and the QBFMs can reflect color features. Extensive experiments on four public databases are done to verify performances of the proposed algorithm. The results demonstrate that the proposed algorithm is superior to some baseline algorithms in content authentication.