Reversible Data Hiding in Encrypted Images using Chunk Encryption and Redundancy Matrix Representation

For reversible data hiding in encrypted images (RDHEI), the private information in the original image content is protected and the embedded secret data can be used to manage the encrypted image. Up to now, many RDHEI algorithms have been reported, however, these reported RDHEI algorithms cannot achi...

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Veröffentlicht in:IEEE transactions on dependable and secure computing 2022-03, Vol.19 (2), p.1382-1394
Hauptverfasser: Liu, Zi-Long, Pun, Chi-Man
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description For reversible data hiding in encrypted images (RDHEI), the private information in the original image content is protected and the embedded secret data can be used to manage the encrypted image. Up to now, many RDHEI algorithms have been reported, however, these reported RDHEI algorithms cannot achieve a high embedding capacity. Therefore, this article proposes a new RDHEI scheme, which will have improved performance. The main contributions of the proposed scheme include two points: chunk encryption (CE) and redundancy matrix representation (RMR). First, different from the encryption method of pixel-by-pixel encryption used in the previous RDHEI algorithms, CE is the method that uses the traditional exclusive-or (XOR) method to encrypt the original image in chunks. By using the CE method, redundancy will be retained in the encrypted chunks. Second, the RMR method is designed to generate available room for accommodating secret data in redundancy matrices, which are existed in some encrypted chunks. Experimental results show that the proposed scheme achieves a high embedding capacity and the directly decrypted images retain high quality.
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subjects Algorithms
chunk encryption
Classification algorithms
Data mining
Embedding
encrypted image
Encryption
Histograms
Image quality
Matrix representation
Pixels
Prediction algorithms
Redundancy
redundancy matrix representation
Reversible data hiding
title Reversible Data Hiding in Encrypted Images using Chunk Encryption and Redundancy Matrix Representation
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