Digital steganography embeds secret messages into images via invisible modifications, posing challenges for steganalysis, which seeks to detect these alterations by analyzing subtle shifts in image distributions. Previous steganalysis efforts primarily focus on enhancing the steganographic signal while suppressing image semantic content, such as through high-pass filtering. However, these empirically designed methods often lack theoretical underpinnings, exhibiting reduced detection accuracy, particularly at low embedding capacities. To address these limitations, we propose an innovative steganalysis approach that transforms images into pure Gaussian noise representations, actively amplifying the subtle distribution shifts introduced by the steganographic processes in spatial images. This paper pioneers the application of diffusion models to magnify steganographic signals, proposing a new paradigm for further research. Specifically, we iteratively perform forward steps of the probability flow in diffusion models to diminish semantic information. By utilizing the natural spreading properties of the diffusion process, we have theoretically validated the efficacy of each forward step in amplifying differences in noise patterns between cover and stego samples. These magnified differences can be easily captured by a simple classifier—a two-layer MLP. Extensive experiments demonstrate the effectiveness of our method, highlighting detection accuracy gains of 10% to 20% under standard conditions and an average 6.7% increase at low embedding rates compared to existing schemes.

Bitwise Vision AutoRegressive (BVAR) Model, as a distinguished source of young blood, has been taking the lead in the track of text-to-image synthesis, which at the same time raises legal and ethnic concerns such as copyright and authenticity. However, existing methods mainly focus on watermarking within diffusion models, which rely on the distinctive attributes of diffusion steps and cannot be directly transferred to new circumstances. To this end, we propose Safe-BVAR, the first watermark framework to embed bit strings during image generation in BVAR. Our study discovers the local similarity of the inferenced latent feature and the element-wise robustness of image autoencoder. Therefore, combined with the residual-accumulative nature of BVAR, we propose a novel Late Stage Residual Implanter to embed watermark and extract the information based on Local Contextual Extractor. Furthermore, we propose a Distributed Rotational Arranger to enhance watermark against local distortions. Our method is training-free and plug-and-play. Meanwhile, it can be easily applied to flexible-sized images. We evaluate the robustness and invisibility of the watermark, showing that it can resist common image attacks and cast inappreciable influence on the image.