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DOI: 10.14569/IJACSA.2024.0151293
PDF

Enhancing Steganography Security with Generative AI: A Robust Approach Using Content-Adaptive Techniques and FC DenseNet

Author 1: Ayyah Abdulhafidh Mahmoud Fadhl
Author 2: Bander Ali Saleh Al-rimy
Author 3: Sultan Ahmed Almalki
Author 4: Tami Alghamdi
Author 5: Azan Hamad Alkhorem
Author 6: Frederick T. Sheldon

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 12, 2024.

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Abstract: Content-adaptive image steganography based on minimizing the additive distortion function and Generative Ad-versarial Networks (GAN) is a promising trend. This approach can quickly generate an embedding probability map and has a higher security performance than hand-crafted methods. however, existing works have ignored the semantic information between neighbouring pixels and the NaN-loss scenarios, which leads to improper convergence. Such cases will degrade the generated Stego images’ quality, decreasing the secret payload’s security. FT GAN performance, which incorporates feature reuse in generator architecture, has been investigated by proposing the FC DenseNet-based generator herein. This investigation explores the superior semantic segmentation capabilities of FC DenseNet, including feature reuse, implicit deep supervision, and the vanishing gradient problem alleviation of DenseNet, toward enhancing visual results, increasing security performance, and accelerating training. The ability to maintain high-quality visual characteristics and robust security even in resource-constrained environments, such as Internet of Things (IoT) contexts, demonstrates the practical benefits of this approach. The qualitative analysis of the visual results regarding the texture regions’ localization and intensity exhibited augmented visual quality. Moreover, an improvement in the security attribute of 0.66% has also been demonstrated regarding average detection errors made by the SRM EC Steganalyzer across all target payloads.

Keywords: Content adaptive; distortion function; GAN; FC DenseNet; steganography; steganalysis

Ayyah Abdulhafidh Mahmoud Fadhl, Bander Ali Saleh Al-rimy, Sultan Ahmed Almalki, Tami Alghamdi, Azan Hamad Alkhorem and Frederick T. Sheldon, “Enhancing Steganography Security with Generative AI: A Robust Approach Using Content-Adaptive Techniques and FC DenseNet” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151293

@article{Fadhl2024,
title = {Enhancing Steganography Security with Generative AI: A Robust Approach Using Content-Adaptive Techniques and FC DenseNet},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151293},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151293},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Ayyah Abdulhafidh Mahmoud Fadhl and Bander Ali Saleh Al-rimy and Sultan Ahmed Almalki and Tami Alghamdi and Azan Hamad Alkhorem and Frederick T. Sheldon}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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