Exploring Diffusion-Inspired Pixel Predictors for WS Steganalysis

Exploring Diffusion-Inspired Pixel Predictors for WS Steganalysis

연구 분야: Strategies

학회: IH&MMSec '24: Proceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security

Analytical estimators of the steganographic change rate in images, such as WS steganalysis, often operate on the noise residual. The residual can be obtained by estimating the cover content with pixel predictors and subtracting it from the image under analysis. In recent years, we have witnessed the success of new deep learning-based denoisers, such as U-Net, in various fields of image processing. In this study, we revisit WS steganalysis using a U-Net variant as a drop-in replacement for the linear filters originally proposed for cover prediction. A novel property of this U-Net variant is its hand-crafted loss function, which ensures that when predicting from stego images, the prediction errors are uncorrelated with the stego noise, an assumption required by WS steganalysis. Improving especially in the textured regions, the proposed predictor produces accurate and consistent change rate estimates. When used as a detector, our model significantly reduces false positives and thus potentially sets a new baseline for LSB replacement steganalysis.