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 …

We consider the generic problem of detecting low-level structures in images, which includes
segmenting the manipulated parts, identifying out-of-focus pixels, separating shadow …

This paper studies the BERT pretraining of video transformers. It is a straightforward but
worth-studying extension given the recent success from BERT pretraining of image …

Built on top of self-attention mechanisms, vision transformers have demonstrated
remarkable performance on a variety of vision tasks recently. While achieving excellent …

Abstract Multi-scale Vision Transformer (ViT) has emerged as a powerful backbone for
computer vision tasks, while the self-attention computation in Transformer scales …

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 …

The widespread dissemination of Deepfakes demands effective approaches that can detect
perceptually convincing forged images. In this paper, we aim to capture the subtle …

Exploring dense matching between the current frame and past frames for long-range context
modeling, memory-based methods have demonstrated impressive results in video object …

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

Since image editing methods in real world scenarios cannot be exhausted, generalization is
a core challenge for image manipulation detection, which could be severely weakened by …