  From its acquisition in the camera sensors to its storage, different
operations are performed to generate the final image. This pipeline imprints
specific traces into the image to form a natural watermark. Tampering with an
image disturbs these traces; these disruptions are clues that are used by most
methods to detect and locate forgeries. In this article, we assess the
capabilities of diffusion models to erase the traces left by forgers and,
therefore, deceive forensics methods. Such an approach has been recently
introduced for adversarial purification, achieving significant performance. We
show that diffusion purification methods are well suited for counter-forensics
tasks. Such approaches outperform already existing counter-forensics techniques
both in deceiving forensics methods and in preserving the natural look of the
purified images. The source code is publicly available at
https://github.com/mtailanian/diff-cf.
