  The proliferation of image manipulation for unethical purposes poses
significant challenges in social networks. One particularly concerning method
is Image Steganography, allowing individuals to hide illegal information in
digital images without arousing suspicions. Such a technique pose severe
security risks, making it crucial to develop effective steganalysis methods
enabling to detect manipulated images for clandestine communications. Although
significant advancements have been achieved with machine learning models, a
critical issue remains: the disparity between the controlled datasets used to
train steganalysis models against real-world datasets of forensic
practitioners, undermining severely the practical effectiveness of standardized
steganalysis models. In this paper, we address this issue focusing on a
realistic scenario where practitioners lack crucial information about the
limited target set of images under analysis, including details about their
development process and even whereas it contains manipulated images or not. By
leveraging geometric alignment and distribution matching of source and target
residuals, we develop TADA (Target Alignment through Data Adaptation), a novel
methodology enabling to emulate sources aligned with specific targets in
steganalysis, which is also relevant for highly unbalanced targets. The
emulator is represented by a light convolutional network trained to align
distributions of image residuals. Experimental validation demonstrates the
potential of our strategy over traditional methods fighting covariate shift in
steganalysis.
