High-Capacity Framework for Reversible Data Hiding in Encrypted Image Using Pixel Predictions and Entropy Encoding

Yingqiang Qiu, Yuyan Yang, Qichao Ying, Huanqiang Zeng, Zhenxing Qian

Previous reversible data hiding in encrypted images (RDHEI) schemes can be either carried out by vacating room before or after data encryption, which leads to a separation of the search field in RDHEI. Besides, high capacity relies heavily on vacating room before encryption (VRBE), which significantly lowers the payload of vacating room after encryption (VRAE) based schemes. To address this issue, this paper proposes a framework for high-capacity RDHEI for both VRBE and VRAE cases using pixel predictions and entropy encoding. We propose an embedding room generation algorithm to produce vacated room by generating the prediction-error histogram (PEH) of the selected cover using adjacency prediction and the median edge detector (MED). In the VRBE scenario, we propose a scheme that generates the embedding room using the proposed algorithm, and encrypts the preprocessed image by using the stream cipher with two encryption keys. In the VRAE scenario, we propose a scheme that involves an improved block modulation and permutation encryption algorithm where the spatial redundancy in the plain-text image can be largely preserved. Then the proposed algorithm is applied on the encrypted image to generate the embedding room. At the data hider's side of both the schemes, the data hider locates the embedding room and embeds the encrypted additional data. On receiving the marked encrypted image, the receivers with different authentication can respectively conduct error-free data extraction and/or error-free image recovery. The experimental results show that the two schemes in the proposed framework can outperform many previous state-of-the-art RDHEI arts. Besides, the proposed schemes can ensure high information security in that little detail of the original image can be directly discovered from the encrypted images or the marked encrypted images.

Knowledge Graph

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