Deepfake technology uses auto-encoders and generative adversarial networks to replace or
artificially construct fine-tuned faces, emotions, and sounds. Although there have been …

Creating fake images and videos such as “Deepfake” has become much easier these days
due to the advancement in Generative Adversarial Networks (GANs). Moreover, recent …

DeepFake can forge high-quality tampered images and videos that are consistent with the
distribution of real data. Its rapid development causes people's panic and reflection. In this …

Deepfake image detection has emerged as an important area of research due to its wide-
ranging implications for various security systems. In particular, in the field of deep learning …

Significant advances in deep learning have obtained hallmark accuracy rates for various
computer vision applications. However, advances in deep generative models have also led …

Public concerns about deepfake face forgery are continually rising in recent years. Most
deepfake detection approaches attempt to learn discriminative features between real and …

The widespread dissemination of facial forgery technology has brought many ethical issues
and aroused widespread concern in society. Most research today treats deepfake detection …

Fake detection has become an urgent task. Generative adversarial networks (GANs)
extended to deep learning has shown its extraordinary ability in the fields of image, audio …

Deepfakes are realistic-looking fake media generated by deep-learning algorithms that
iterate through large datasets until they have learned how to solve the given problem (ie …

With the advent of information and communication technologies, there have been
breakthrough developments in the field of Artificial Intelligence (AI). Moreover, increasing …