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Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

Xiong Zhang1,2, Minqing Zhang1,2,3,*, Xu An Wang1,2,3, Wen Jiang1,2, Chao Jiang1,2, Pan Yang1,4

1 College of Cryptography Engineering, Engineering University of People’s Armed Police, Xi’an, 710086, China
2 Key Laboratory of People’s Armed Police for Cryptology and Information Security, Xi’an, 710086, China
3 Key Laboratory of CTC & Information Engineering (Engineering University of People’s Armed Police), Ministry of Education, Xi’an, 710086, China
4 Staff Department of People’s Armed Police Ningxia Corps, Yinchuan, 750000, China

* Corresponding Author: Minqing Zhang. Email: email

(This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)

Computers, Materials & Continua 2024, 79(2), 1925-1938. https://doi.org/10.32604/cmc.2024.050899

Abstract

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission. The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission. The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data. This process effectively enhances the concealment and imperceptibility of confidential information, thereby improving the security of such information during transmission and reducing security risks. Furthermore, we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments. Through adversarial training, the algorithm is strengthened to enhance its resilience against attacks and overall robustness, ensuring better protection against potential threats. Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity. Additionally, it ensures the quality and fidelity of the stego image. The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.

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Cite This Article

APA Style
Zhang, X., Zhang, M., Wang, X.A., Jiang, W., Jiang, C. et al. (2024). Robust information hiding based on neural style transfer with artificial intelligence. Computers, Materials & Continua, 79(2), 1925-1938. https://doi.org/10.32604/cmc.2024.050899
Vancouver Style
Zhang X, Zhang M, Wang XA, Jiang W, Jiang C, Yang P. Robust information hiding based on neural style transfer with artificial intelligence. Comput Mater Contin. 2024;79(2):1925-1938 https://doi.org/10.32604/cmc.2024.050899
IEEE Style
X. Zhang, M. Zhang, X.A. Wang, W. Jiang, C. Jiang, and P. Yang "Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence," Comput. Mater. Contin., vol. 79, no. 2, pp. 1925-1938. 2024. https://doi.org/10.32604/cmc.2024.050899



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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