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 …

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 …

As realistic facial manipulation technologies have achieved remarkable progress, social
concerns about potential malicious abuse of these technologies bring out an emerging …

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

Although current face manipulation techniques achieve impressive performance regarding
quality and controllability, they are struggling to generate temporal coherent face videos. In …

The current spike of hyper-realistic faces artificially generated using deepfakes calls for
media forensics solutions that are tailored to video streams and work reliably with a low false …

The free access to large-scale public databases, together with the fast progress of deep
learning techniques, in particular Generative Adversarial Networks, have led to the …

AI-synthesized face-swap** videos, commonly known as DeepFakes, is an emerging
problem threatening the trustworthiness of online information. The need to develop and …