Abstract Generative Adversarial Networks (GAN) have led to the generation of very realistic
face images, which have been used in fake social media accounts and other disinformation …

Deepfakes present an emerging threat in cyberspace. Recent developments in machine
learning make deepfakes highly believable, and very difficult to differentiate between what is …

Although effective deepfake detection models have been developed in recent years recent
studies have revealed that these models can result in unfair performance disparities among …

Generative adversarial network (GAN) generated high-realistic human faces are visually
challenging to discern from real ones. They have been used as profile images for fake social …

Face manipulation techniques, especially DeepFake techniques, are causing severe social
concerns and security problems. When faced with skewed data distributions such as those …

Despite the development of effective deepfake detectors in recent years, recent studies have
demonstrated that biases in the data used to train these detectors can lead to disparities in …

With the development of the Generative Adversarial Networks (GANs) and DeepFakes, AI-
synthesized images are now of such high quality that humans can hardly distinguish them …

Deepfake technology uses artificial intelligence to create realistic but false audio, images,
and videos. Deepfake technology poses a significant threat to the authenticity of visual …

Benefiting from the tremendous success of Generative Adversarial Networks (GANs), we are
entering a world where it will be increasingly difficult for people to distinguish whether an …

With the rapid development of deep generative models (such as Generative Adversarial
Networks and Diffusion models), AI-synthesized images are now of such high quality that …