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 …

Differences in forgery attributes of images generated in CNN-synthesized and image-editing
domains are large, and such differences make a unified image forgery detection and …

To defend against manipulation of image content, such as splicing, copy-move, and
removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to …

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 …

The spread of misinformation through synthetically generated yet realistic images and
videos has become a significant problem, calling for robust manipulation detection methods …

In the last years, due to the availability and easy of use of image editing tools, a large
amount of fake and altered images have been produced and spread through the media and …

We present a novel framework, Spatial Pyramid Attention Network (SPAN) for detection and
localization of multiple types of image manipulations. The proposed architecture efficiently …

Digital image authenticity has promoted research on image forgery localization. Although
deep learning-based methods achieve remarkable progress, most of them usually suffer …

We propose a method for detecting face swap** and other identity manipulations in single
images. Face swap** methods, such as DeepFake, manipulate the face region, aiming to …