Generative image steganography is a technique that conceals secret messages within generated images, without relying on pre-existing cover images. Recently, a number of diffusion model-based generative image steganography (DM-GIS) methods have been introduced, which effectively combat traditional steganalysis techniques. In this paper, we identify the key factors that influence DM-GIS security and revisit the security of existing methods. Specifically, we first provide an overview of the general pipelines of current DM-GIS methods, finding that the noise space of diffusion models serves as the primary embedding domain. Further, we analyze the relationship between DM-GIS security and noise distribution of diffusion models, theoretically demonstrating that any steganographic operation that disrupts the noise distribution compromise DM-GIS security. Building on this insight, we propose a Noise Space-based Diffusion Steganalyzer (NS-DSer)-a simple yet effective steganalysis framework allowing for detecting DM-GIS generated images in the diffusion model noise space. We reevaluate the security of existing DM-GIS methods using NS-DSer across increasingly challenging detection scenarios. Experimental results validate our theoretical analysis of DM-GIS security and show the effectiveness of NS-DSer across diverse detection scenarios.

翻译：生成式图像隐写是一种无需依赖现有载体图像、直接在生成图像中隐藏秘密信息的技术。近期，多种基于扩散模型的生成式图像隐写方法被提出，这些方法能有效对抗传统隐写分析技术。本文系统分析了影响DM-GIS安全性的关键因素，并对现有方法的安全性进行重新审视。具体而言，我们首先梳理了当前DM-GIS方法的通用流程，发现扩散模型的噪声空间是主要的嵌入域。进一步，我们分析了DM-GIS安全性与扩散模型噪声分布的关系，从理论上证明任何破坏噪声分布的隐写操作都会损害DM-GIS的安全性。基于这一发现，我们提出一种基于噪声空间的扩散隐写分析器——NS-DSer，这是一个简洁而有效的隐写分析框架，可在扩散模型噪声空间中检测DM-GIS生成的图像。我们使用NS-Dser在日益复杂的检测场景下对现有DM-GIS方法的安全性进行了重新评估。实验结果验证了我们对DM-GIS安全性的理论分析，并证明了NS-DSer在不同检测场景下的有效性。