With the rapid progress in recent years, techniques that generate and manipulate
multimedia content can now provide a very advanced level of realism. The boundary …

We are currently on a verge of a revolution in digital photography. Developments in
computational imaging and adoption of artificial intelligence have spawned new editing …

With generative models proliferating at a rapid rate, there is a growing need for general
purpose fake image detectors. In this work, we first show that the existing paradigm, which …

In this work we ask whether it is possible to create a" universal" detector for telling apart real
images from these generated by a CNN, regardless of architecture or dataset used. To test …

The free access to large-scale public databases, together with the fast progress of deep
learning techniques, in particular Generative Adversarial Networks, have led to the …

Recent advances in image editing techniques have posed serious challenges to the
trustworthiness of multimedia data, which drives the research of image tampering detection …

In this paper we present TruFor, a forensic framework that can be applied to a large variety
of image manipulation methods, from classic cheapfakes to more recent manipulations …

High quality face editing in videos is a growing concern and spreads distrust in video
content. However, upon closer examination, many face editing algorithms exhibit artifacts …

To fight against real-life image forgery, which commonly involves different types and
combined manipulations, we propose a unified deep neural architecture called ManTra-Net …

Image manipulation detection is different from traditional semantic object detection because
it pays more attention to tampering artifacts than to image content, which suggests that richer …