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 …

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 …

With the powerful deep network architectures, such as generative adversarial networks, one
can easily generate photorealistic images. Although the generated images are not …

Local descriptors based on the image noise residual have proven extremely effective for a
number of forensic applications, like forgery detection and localization. Nonetheless …

In recent decades, a significant research effort has been devoted to the development of
forensic tools for retrieving information and detecting possible tampering of multimedia …

Many forensic techniques have recently been developed to determine whether an image
has undergone a specific manipulation operation. When multiple consecutive operations are …

Generative Adversarial Network (GAN) is a prominent generative model that are widely used
in various applications. Recent studies have indicated that it is possible to obtain fake face …

Image inpainting has been substantially improved with deep learning in the past years.
Deep inpainting can fill image regions with plausible contents, which are not visually …

Last-generation GAN models allow to generate synthetic images which are visually
indistinguishable from natural ones, raising the need to develop tools to distinguish fake and …

Image forgery detection has aroused widespread research interest in both academia and
industry because of its potential security threats. Existing forgery detection methods achieve …