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 …

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 …

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 …

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 …

In this paper we propose a novel image representation called face X-ray for detecting
forgery in face images. The face X-ray of an input face image is a greyscale image that …

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 …

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 …

The quality of image generation and manipulation is reaching impressive levels, making it
increasingly difficult for a human to distinguish between what is real and what is fake …