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New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity

The main challenge in steganography algorithms is balancing between the size of the secret message (SM) that is embedded in the cover image (CI) and the quality of the stego-image (SI). This manuscript proposes a new steganography algorithm to hide a large amount of secret messages in cover images w...

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Autores principales: Hardan, Hanan, Alawneh, Ali, El-Emam, Nameer N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680874/
https://www.ncbi.nlm.nih.gov/pubmed/36426248
http://dx.doi.org/10.7717/peerj-cs.1115
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author Hardan, Hanan
Alawneh, Ali
El-Emam, Nameer N.
author_facet Hardan, Hanan
Alawneh, Ali
El-Emam, Nameer N.
author_sort Hardan, Hanan
collection PubMed
description The main challenge in steganography algorithms is balancing between the size of the secret message (SM) that is embedded in the cover image (CI) and the quality of the stego-image (SI). This manuscript proposes a new steganography algorithm to hide a large amount of secret messages in cover images with a high degree of non-perception in the resulting images. The proposed algorithm applied a multi-channel deep data hiding and extraction algorithm (MCDHEA) based on a modified multi-level steganography (MLS) approach. This approach used a new modification of the least significant bits (NMLSB) to make it hard to extract a secret message from attackers. The secret message was distributed among n-blocks; each block was hidden into a sub-channel that included multi-level hiding and flows into the main channel. Different grayscale images were used through the levels of each sub-channel and using the color image in the last level of the main channel. The image size of the multi-level was expanded from one level to the next level, and at each level, lossless image compression using the Huffman coding algorithm was applied to enable the size of the data hiding at the next level. In addition, the encryption of secret messages and intermediate cover images based on the XOR encryption algorithm is applied before the hiding process. Finally, the number of bits to be replaced at each level for both sub and main channels was four bits per byte except at the last level of the main channel based on a new approach using a non-uniform number of bits replacements. This algorithm’s performance was evaluated using various measures. The results show that the proposed technique is effective and better than the previous works concerning imperceptibility and robustness. Furthermore, the results show that the maximum peak signal-to-noise ratio (PSNR) of 61.2 dB for the payload of 18,750 bytes, the maximum video information fidelity (VIF) of 0.95 for the payload of 19,660 bytes, and the maximum structural similarity index measure (SSIM) of 0.999 for the payload of 294,912 bytes.
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spelling pubmed-96808742022-11-23 New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity Hardan, Hanan Alawneh, Ali El-Emam, Nameer N. PeerJ Comput Sci Algorithms and Analysis of Algorithms The main challenge in steganography algorithms is balancing between the size of the secret message (SM) that is embedded in the cover image (CI) and the quality of the stego-image (SI). This manuscript proposes a new steganography algorithm to hide a large amount of secret messages in cover images with a high degree of non-perception in the resulting images. The proposed algorithm applied a multi-channel deep data hiding and extraction algorithm (MCDHEA) based on a modified multi-level steganography (MLS) approach. This approach used a new modification of the least significant bits (NMLSB) to make it hard to extract a secret message from attackers. The secret message was distributed among n-blocks; each block was hidden into a sub-channel that included multi-level hiding and flows into the main channel. Different grayscale images were used through the levels of each sub-channel and using the color image in the last level of the main channel. The image size of the multi-level was expanded from one level to the next level, and at each level, lossless image compression using the Huffman coding algorithm was applied to enable the size of the data hiding at the next level. In addition, the encryption of secret messages and intermediate cover images based on the XOR encryption algorithm is applied before the hiding process. Finally, the number of bits to be replaced at each level for both sub and main channels was four bits per byte except at the last level of the main channel based on a new approach using a non-uniform number of bits replacements. This algorithm’s performance was evaluated using various measures. The results show that the proposed technique is effective and better than the previous works concerning imperceptibility and robustness. Furthermore, the results show that the maximum peak signal-to-noise ratio (PSNR) of 61.2 dB for the payload of 18,750 bytes, the maximum video information fidelity (VIF) of 0.95 for the payload of 19,660 bytes, and the maximum structural similarity index measure (SSIM) of 0.999 for the payload of 294,912 bytes. PeerJ Inc. 2022-10-19 /pmc/articles/PMC9680874/ /pubmed/36426248 http://dx.doi.org/10.7717/peerj-cs.1115 Text en © 2022 Hardan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Algorithms and Analysis of Algorithms
Hardan, Hanan
Alawneh, Ali
El-Emam, Nameer N.
New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity
title New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity
title_full New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity
title_fullStr New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity
title_full_unstemmed New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity
title_short New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity
title_sort new deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680874/
https://www.ncbi.nlm.nih.gov/pubmed/36426248
http://dx.doi.org/10.7717/peerj-cs.1115
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