Deep Learning (DL) is the most efficient technique to handle a wide range of challenging
problems such as data analytics, diagnosing diseases, detecting anomalies, etc. The …

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 …

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 …

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 …

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 …

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 …

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 …

Face forgery detection is raising ever-increasing interest in computer vision since facial
manipulation technologies cause serious worries. Though recent works have reached …

Existing face forgery detection models try to discriminate fake images by detecting only
spatial artifacts (eg, generative artifacts, blending) or mainly temporal artifacts (eg, flickering …