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 …

Seeing is not believing anymore. Different techniques have brought to our fingertips the
ability to modify an image. As the difficulty of using such techniques decreases, lowering the …

As realistic facial manipulation technologies have achieved remarkable progress, social
concerns about potential malicious abuse of these technologies bring out an emerging …

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 …

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 …

Forensic analyses of digital images rely heavily on the traces of in-camera and out-camera
processes left on the acquired images. Such traces represent a sort of camera fingerprint. If …

Advances in photo editing and manipulation tools have made it significantly easier to create
fake imagery, highlighting the need for better visual forensics algorithms. However, learning …

We propose a two-stream network for face tampering detection. We train GoogLeNet to
detect tampering artifacts in a face classification stream, and train a patch based triplet …

Deep generative models have recently achieved impressive results for many real-world
applications, successfully generating high-resolution and diverse samples from complex …

The rapid progress in synthetic image generation and manipulation has now come to a point
where it raises significant concerns for the implications towards society. At best, this leads to …