Secret sharing is a useful method which divides a secret message into several shares for security. During the recovery procedure, only when sufficient shares are obtained, the secret message can be recovered. This paper proposes two novel reversible data hiding schemes in encrypted image via secret sharing over Galois fields ${{GF}}{({p})}$ and ${{GF}}{(}{{2}^{{8}}}{)}$ . The content owner first applies a specific encryption method through block and pixel permutation and Shamir’s secret sharing. Then, the theoretical demonstration is introduced to explain that the generated shares are suitable for data embedding over ${{GF}}{({p})}$ and ${{GF}}{(}{{2}^{{8}}}{)}$ . Finally, two embedding algorithms over ${{GF}}{({p})}$ and ${{GF}}{(}{{2}^{{8}}}{)}$ are presented, and on the receiver side, with different keys, additional data can be extracted correctly and original image can be recovered losslessly. Experimental results show that our schemes can achieve better rate-distortion performance than some state-of-the-art schemes.

This paper proposes an efficient scheme for generating image hashing by combining the local texture and color angle features. During the stage of texture extraction, using Weber’s Law, the difference ratios between the center pixels and their surrounding pixels are calculated and the dimensions of these values are further reduced by applying principal component analysis to the statistical histogram. In the stage of color feature extraction, the color angle of each pixel is computed before dimensional reduction is carried out using a discrete cosine transform and a significant coefficients selection strategy. The main contribution of this paper is a novel construction for image hashing that incorporates texture and color features by using Weber local binary pattern and color angular pattern. Experimental results demonstrate the efficacy of the proposed scheme, especially for perceptual robustness against common content-preserving manipulations, such as JPEG compression, Gaussian low-pass filtering, and image scaling. Based on the comparisons with state-of-the-art schemes, receiver operating characteristic curves and integrated histograms of normalized distances show the superiority of our scheme in terms of robustness and discrimination.