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 …

Over the last few decades, rapid progress in AI, machine learning, and deep learning has
resulted in new techniques and various tools for manipulating multimedia. Though the …

Face forgery by deepfake is widely spread over the internet and has raised severe societal
concerns. Recently, how to detect such forgery contents has become a hot research topic …

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 novel synthetic training data called self-blended images (SBIs) to
detect deepfakes. SBIs are generated by blending pseudo source and target images from …

The remarkable success in face forgery techniques has received considerable attention in
computer vision due to security concerns. We observe that up-sampling is a necessary step …

Current face forgery detection methods achieve high accuracy under the within-database
scenario where training and testing forgeries are synthesized by the same algorithm …

Although current deep learning-based face forgery detectors achieve impressive
performance in constrained scenarios, they are vulnerable to samples created by unseen …

Diffusion models have shown remarkable success in visual synthesis, but have also raised
concerns about potential abuse for malicious purposes. In this paper, we seek to build a …

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 …