Deep learning has been overwhelmingly successful in computer vision (CV), natural
language processing, and video/speech recognition. In this paper, our focus is on CV. We …

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 …

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

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 …

Deep learning has been successfully applied to solve various complex problems ranging
from big data analytics to computer vision and human-level control. Deep learning advances …

Abstract Deepfakes (" deep learning"+" fake") are synthetically-generated videos from AI
algorithms. While they could be entertaining, they could also be misused for falsifying …

Recently, deepfakes have raised severe concerns about the authenticity of online media.
Prior works for deepfake detection have made many efforts to capture the intra-modal …

One of the most pressing challenges for the detection of face-manipulated videos is
generalising to forgery methods not seen during training while remaining effective under …

The recent emergence of machine-manipulated media raises an important societal question:
How can we know whether a video that we watch is real or fake? In two online studies with …

Generative deep learning algorithms have progressed to a point where it is difficult to tell the
difference between what is real and what is fake. In 2018, it was discovered how easy it is to …