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 …

Easy access to audio-visual content on social media, combined with the availability of
modern tools such as Tensorflow or Keras, and open-source trained models, along with …

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 …

With generative models proliferating at a rapid rate, there is a growing need for general
purpose fake image detectors. In this work, we first show that the existing paradigm, which …

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 …

Recent studies in deepfake detection have yielded promising results when the training and
testing face forgeries are from the same dataset. However, the problem remains challenging …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

Existing face forgery detectors mainly focus on specific forgery patterns like noise
characteristics, local textures, or frequency statistics for forgery detection. This causes …

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 …

We present a novel high-fidelity generative adversarial network (GAN) inversion framework
that enables attribute editing with image-specific details well-preserved (eg, background …