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 …

In the last few years, with the advancement of deep learning methods, especially Generative
Adversarial Networks (GANs) and Variational Auto-encoders (VAEs), fabricated content has …

The introduction of publicly available large-scale datasets and advances in generative
adversarial networks (GANs) have revolutionized the generation of hyper-realistic facial …

Face manipulation technology is advancing very rapidly, and new methods are being
proposed day by day. The aim of this work is to propose a deepfake detector that can cope …

Recent advances in face forgery techniques produce nearly visually untraceable deepfake
videos, which could be leveraged with malicious intentions. As a result, researchers have …

Face manipulation techniques develop rapidly and arouse widespread public concerns.
Despite that vanilla convolutional neural networks achieve acceptable performance, they …

Increasingly advanced deepfake approaches have made the detection of deepfake videos
very challenging. We observe that the general deepfake videos often exhibit appearance …

Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection
methods are typically inadequate in generalizability, with a tendency to overfit to image …

The privacy of individuals and entire countries is currently threatened by the widespread use
of face-swapping DeepFake models, which result in a sizable number of fake videos that …

With the rapid development of deep learning and communication technology, the application
of streaming media services and social software have gone deep into life. However, in the …