Plug-and-Hide: Provable and Adjustable Diffusion Generative Steganography

2024-09-07

arXiv.org

AI Summary

Existing diffusion-based generative steganography (DGS) methods produce high-fidelity images but compromise steganographic security by introducing significant perturbations to the initial Gaussian noise, thus failing to jointly optimize image fidelity, security, and message extraction accuracy. Method: We theoretically establish, for the first time, the pivotal role of initial noise normality in balancing these three objectives. We propose Provable and Adjustable Bit-to-Gaussian (PA-B2G), a provably invertible and tunable encoding mechanism that losslessly maps secret messages into standard Gaussian noise. Leveraging probability flow ODEs, we design a plug-and-play architecture compatible with mainstream diffusion models—including Stable Diffusion—without fine-tuning. Results: Our method preserves original image fidelity while substantially enhancing steganographic security; message extraction accuracy consistently exceeds 99.5%. Moreover, it demonstrates strong cross-model generalization capability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Generative Steganography (GS) is a novel technique that utilizes generative models to conceal messages without relying on cover images. Contemporary GS algorithms leverage the powerful generative capabilities of Diffusion Models (DMs) to create high-fidelity stego images. However, these algorithms, while yielding relatively satisfactory generation outcomes and message extraction accuracy, significantly alter modifications to the initial Gaussian noise of DMs, thereby compromising steganographic security. In this paper, we rethink the trade-off among image quality, steganographic security, and message extraction accuracy within Diffusion Generative Steganography (DGS) settings. Our findings reveal that the normality of initial noise of DMs is crucial to these factors and can offer theoretically grounded guidance for DGS design. Based on this insight, we propose a Provable and Adjustable Message Mapping (PA-B2G) approach. It can, on one hand, theoretically guarantee reversible encoding of bit messages from arbitrary distributions into standard Gaussian noise for DMs. On the other hand, its adjustability provides a more natural and fine-grained way to trade off image quality, steganographic security, and message extraction accuracy. By integrating PA-B2G with a probability flow ordinary differential equation, we establish an invertible mapping between secret messages and stego images. PA-B2G can be seamlessly incorporated with most mainstream DMs, such as the Stable Diffusion, without necessitating additional training or fine-tuning. Comprehensive experiments corroborate our theoretical insights regarding the trade-off in DGS settings and demonstrate the effectiveness of our DGS algorithm in producing high-quality stego images while preserving desired levels of steganographic security and extraction accuracy.

Problem

• Balancing image quality, security, and accuracy in steganography
• Ensuring normality of initial noise in diffusion models
• Reversible encoding of messages into Gaussian noise

Innovation

• Provable and Adjustable Message Mapping (PA-B2G)
• Reversible encoding into standard Gaussian noise
• Seamless integration with mainstream Diffusion Models