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SteganoDDPM: A high-quality image steganography self-learning method using diffusion model

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摘要: Image steganography has become a focal point of interest for researchers due to its capacity for the covert transmission of sensitive data. Traditional diffusion models often struggle with image steganography tasks involving paired data, as their core principle of gradually removing noise is not directly suited for maintaining the correspondence between carrier and secret information. To address this challenge, this paper conducts an in-depth analysis of the principles behind diffusion models and proposes a novel framework for an image steganography diffusion model. The study begins by mathematically representing the steganography tasks of paired images, introducing two optimization objectives: minimizing the secrecy leakage function and embedding distortion function. Subsequently, it identifies three key issues that need to be addressed in paired image steganography tasks and, through specific constraint mechanisms and optimization strategies, enables the diffusion model to effectively handle paired data. This enhances the quality of the generated stego-images and resolves issues such as image clarity. Finally, on public datasets like CelebA, the proposed model is compared with existing generation model-based image steganography techniques, analyzing its implementation effects and performance parameters. Experimental results indicate that, compared to current technologies, the model framework proposed in this study not only improves image quality but also achieves significant enhancements in multiple performance metrics, including the imperceptibility and anti-detection capabilities of the images. Specifically, the PSNR of its stego-images reaches 93.14dB, and the extracted images’ PSNR reaches 91.23dB, an approximate improvement of 30% over existing technologies; the attack success rate is reduced to 2.4x10-38. These experimental outcomes validate the efficacy and superiority of the method in image steganography tasks.

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[V1] 2024-04-23 09:55:31 ChinaXiv:202404.00287V1 下载全文
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