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 …

Abstract Online Social Networks are perpetually evolving and used in plenteous
applications such as content sharing, chatting, making friends/followers, customer …

In this paper, we analyse the generalization ability of binary classifiers for the task of
deepfake detection. We find that the stumbling block to their generalization is caused by the …

With the rapid development of face manipulation technology, it is difficult for human eyes to
distinguish fake face images. On the contrary, Convolutional Neural Network (CNN) …

The creation or manipulation of facial appearance through deep generative approaches,
known as DeepFake, have achieved significant progress and promoted a wide range of …

Recently, deepfake videos, generated by deep learning algorithms, have attracted
widespread attention. Deepfake technology can be used to perform face manipulation with …

In the rapidly evolving digital landscape, the generation of fake visual, audio, and textual
content poses a significant threat to the trust of society, political stability, and integrity of …

On existing public benchmarks, face forgery detection techniques have achieved great
success. However, when used in multi-person videos, which often contain many people …

Recent advances in the field of machine learning and social media platforms facilitate the
creation and rapid dissemination of realistic fake content (ie, images, videos, audios) …

A variety of effective face-swap and face-reenactment methods have been publicized in
recent years, democratizing the face synthesis technology to a great extent. Videos …