Leveraging Fluctuations of Black-Box Generative Models for Secure Deep Image Steganography

作者：Xiangkun Wang, Kejiang Chen, Jiansong Zhang, Weiming Zhang, Nenghai Yu · 发表于：IEEE Transactions on Dependable and Secure Computing · 年份：2025 · DOI：10.1109/tdsc.2025.3636663 · 被引用次数：1 · 研究领域：Advanced Steganography and Watermarking Techniques、Generative Adversarial Networks and Image Synthesis、Chaos-based Image/Signal Encryption

Image steganography is an essential technique for concealing information by embedding secret information within images to make it undetectable. In recent years, with the rapid development and popularization of text-to-image generation models, many generated images have been disseminated through the Internet, thus making generated images ideal covers for steganography. Given that the distribution of generated images is more easily modeled than natural images, steganographic methods based on generated images exhibit higher security. Nevertheless, these methods typically require white-box access to the generative model, while contemporary popular generative models are black-box models. We observed that slight modifications in the input parameters of black-box image generative models result in subtle differences between generated images, offering new camouflage advantages for image steganography. Based on this observation, we propose an image steganography method based on the fluctuation of generative models. This approach leverages the fluctuation of image generative models, disguising stego images to appear as if they were generated by the parameter fluctuations of the generative model. Experimental results show that our proposed method outperforms baseline methods when facing steganalysis attacks, significantly enhancing steganographic security without compromising image quality.