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- W3048862016 abstract "Reversible Data Hiding in Encrypted Images (RDH-EI) has gained much popularity in the field of signal processing and cloud computing. In this study, we propose a high-capacity RDH-EI scheme using multi-layer embedding. It ensures that after extraction of the embedded additional data from the marked encrypted image, the original image is recovered completely. Broadly, the method comprises three parties: the content owner, the data hider/ cloud owner, and the receiver. In the beginning, the image is encrypted by the content owner using encryption based on the chaotic behaviour of PWL-memristor and sent to the data hider. At the data hider’s side, even-odd value embedding technique is applied in multiple layers of the encrypted image to increase the embedding capacity. Meanwhile, the auxiliary information consisting of location map (LM) for each layer and the number of layers, to be used in the recovery phase, is recorded during the embedding. After completing the embedding procedure, permutation operation is applied on the marked encrypted image to prevent the perceptual information leakage. Finally, at the receiver’s side the embedded additional data and the original image is recovered losslessly with the help of valid keys and shared auxiliary information. From the experimental results, it is observed that the quality of the directly decrypted image is 51.0 dB or above. The advantage of the proposed scheme is that the PSNR is almost same for each layer, i.e. even for the n number of layers used, the PSNR is 51.0 dB or above. Moreover, we applied chosen plaintext attack to test the encryption function and in case of any cropping attack on the marked encrypted image during the transfer, the cover image can be recovered with less distortion." @default.
- W3048862016 created "2020-08-18" @default.
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- W3048862016 date "2020-01-01" @default.
- W3048862016 modified "2023-10-18" @default.
- W3048862016 title "High-Capacity Reversible Data Hiding in Encrypted Images Using Multi-Layer Embedding" @default.
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- W3048862016 doi "https://doi.org/10.1109/access.2020.3015882" @default.
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