Kavli Affiliate: Yi Zhou
| First 5 Authors: Jiahao Zhu, Zixuan Chen, Lingxiao Yang, Xiaohua Xie, Yi Zhou
| Summary:
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.
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