Targeted Pooled Latent-Space Steganalysis Applied to Generative Steganography, with a Fix

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 also show how, by randomly sampling the norm of the latent vector before generation, the initial Stego scheme becomes undetectable in the latent space.

Dual JPEG Compatibility: a Reliable and Explainable Tool for Image Forensics

Given a JPEG pipeline (compression or decompression), this paper shows how to find the antecedent of a 8 x 8 block. If it exists, the block is compatible with the pipeline. For unaltered images, all blocks are always compatible with the original pipeline; however, for manipulated images, this is not always the case. This article demonstrates the potential of compatibility concepts for JPEG image forensics. It presents a solution to the main challenge of finding a block antecedent in a high-dimensional space. This solution relies on a local search algorithm with restrictions on the search space. We show that inpainting, copy-move, or splicing applied after a JPEG compression can be turned into three different mismatch problems and be detected. In particular, when the image is re-compressed after the modification, we can detect the manipulation if the quality factor of the second compression is higher than the first one. Our method can pinpoint forgeries down to the JPEG block with great detection power and without False Positive. We compare our method with two state-of-the-art models on localizing inpainted forgeries after a simple or a double compression. We show that under our working assumptions, it outperforms those models for most experiments.