5 accepted papers
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 distri…
This paper proposes an original approach for the automatic detection of AI-generated images, using features derived from noise residuals artefacts. Contrary to most current research that leverages sophisticated deep learning models to further improve performance, this study highlights the distinct n…
The current art in schemes using deflection criterion such as Mi-POD for JPEG steganography is either under-performing or on par with distortion-based schemes. We link this lack of performance to a poor estimation of the variance of the model of the noise on the cover image. In this paper, we propos…
This paper deeply studies the principle of the recent reverse JPEG compatibility attack [1]. This analysis allows us to cast the problem of hidden data detection in DCT coefficients within hypothesis testing theory. The optimal LR test, thought efficient, is rather computationally expensive. Therefo…
This paper investigates practical strategies for distributing payload across images with content-adaptive steganography and for pooling outputs of a single-image detector for steganalysis. Adopting a statistical model for the detector's output, the steganographer minimizes the power of the most powe…