Steganographic schemes dedicated to generated images modify the seed vector in the latent space to embed a message. Whereas most steganalysis methods attempt to detect the embedding in the image space, this paper proposes to perform steganalysis in the latent space by modeling the statistical distribution of the norm of the latent vector. Specifically, we analyze the practical security of a scheme proposed by Hu et al. for latent diffusion models, which is both robust and practically undetectable when steganalysis is performed on generated images. We show that after embedding, the Stego (latent) vector is distributed on a hypersphere while the Cover vector is i.i.d. Gaussian. By going from the image space to the latent space, we show that it is possible to model the norm of the vector in the latent space under the Cover or Stego hypothesis as Gaussian distributions with different variances. A Likelihood Ratio Test is then derived to perform pooled steganalysis. The impact of the potential knowledge of the prompt and the number of diffusion steps is also studied. Additionally, we show how, by randomly sampling the norm of the latent vector before generation, the initial Stego scheme becomes undetectable in the latent space.

翻译：专用于生成图像的隐写方案通过修改潜在空间中的种子向量来嵌入信息。尽管大多数隐写分析方法试图在图像空间中检测嵌入，本文提出通过在潜在空间中对潜在向量范数的统计分布进行建模来执行隐写分析。具体而言，我们分析了Hu等人针对潜在扩散模型提出的方案的实际安全性，该方案在生成图像上进行隐写分析时既具有鲁棒性又实际不可检测。我们证明嵌入后，隐写（潜在）向量分布在一个超球面上，而载体向量服从独立同分布的高斯分布。通过从图像空间转换到潜在空间，我们证明了可以在载体或隐写假设下，将潜在空间中向量的范数建模为具有不同方差的高斯分布。随后推导出似然比检验以执行池化隐写分析。本文还研究了潜在提示词知识和扩散步骤数量的影响。此外，我们展示了通过在生成前随机采样潜在向量的范数，初始隐写方案在潜在空间中变得不可检测。