Reversible Data Hiding for Encrypted Images Based on Statistical Learning

In this paper, we propose a novel reversible data hiding RDH approach for encrypted images by using statistical learning. To hide the data, a new random permutation algorithm is proposed using a high-speed stream cipher to secure the data hiding process. A secret message is embedded into the permuted image blocks based on a checkerboard pattern, by modifying the least significant encrypted bits. To detect the hidden data, prior works utilize a single spatial correlation. In contrast, our approach is novel in that it uses a high-dimensional statistical feature vector upon which a new boosting algorithm for high reversibility is proposed. A complete encoding and decoding procedure of RDH for encrypted images is elaborated. The experimental results show that the proposed method can detect secret message bits and restore the original image simultaneously with $$100\,\%$$ reversibility with a higher capacity, significantly outperforming the state-of-the-art RDH methods on encrypted images.

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