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 …

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 …

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 …

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 …

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 …

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

We propose a new method to detect deepfake images using the cue of the source feature
inconsistency within the forged images. It is based on the hypothesis that images' distinct …