Image steganography based on least bias generative adversarial network

Jing Duan; Jie Duan; Yanhua Wang; Xuefeng Wan

doi:10.1117/12.2640734

13 October 2022 Image steganography based on least bias generative adversarial network

Jing Duan, Jie Duan, Yanhua Wang, Xuefeng Wan

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871D (2022) https://doi.org/10.1117/12.2640734
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

The main improvement of image steganography is that it can generate dense images with high visual quality. The generated countermeasure network based on least squares is more stable than the conventional generated countermeasure network and converges faster than WGAN. The main work of the least squares generation countermeasure network is to replace the cross-entropy loss function with the least squares loss function. This is considered to improve two problems of the traditional Gan, that is, the image quality generated by the traditional Gan is not high, and the training process is very unstable. The least squares generation countermeasure network attempts to use different distance metrics to build a more stable, convergent and high-quality countermeasure network. In the process of network training, the human visual model is used to evaluate and restrict the visual quality of the secret image, and enhance the concealment of steganography from the perspective of improving the visual quality of the secret image. In the process of steganalysis of the secret image, the secret image is evaluated in combination with the method of image forensics, so as to ensure the visual quality of the image and reduce the trace of image modification, Enhance the concealment of image steganography.

Citation Download Citation

Jing Duan, Jie Duan, Yanhua Wang, and Xuefeng Wan "Image steganography based on least bias generative adversarial network", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871D (13 October 2022); https://doi.org/10.1117/12.2640734

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KEYWORDS

Steganography

Image quality

Image enhancement

Gallium nitride

Steganalysis

Network architectures

Image processing

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