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 …

Advancements in deep learning techniques and the availability of free, large databases
have made it possible, even for non-technical people, to either manipulate or generate …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

With the development of Metaverse technology, the avatar in Metaverse has faced serious
security and privacy concerns. Analyzing facial features to distinguish between genuine and …

Hyper-realistic images that are not differentiable from authentic images to regular viewers
have become extremely easy to generate and highly accessible. Furthermore, the …

The emergence of deep learning has sparked notable strides in the quality of synthetic
media. Yet, as photorealism reaches new heights, the line between generated and authentic …

Abstract Machine and deep learning models are now one of the most valuable tools in the
arsenal of computer security practitioners. Their success has been demonstrated in various …

Abstract Massive advancements in Generative Artificial Intelligence in the recent years, have
introduced hyper-realistic fake multimedia content. Where such technologies have become …

Due to the appeal of Generative Adversarial Networks (GANs), synthetic face images
generated with GAN models are difficult to differentiate from genuine human faces and may …

随着深度网络生成式伪造人脸技术的迅速传播, 不法分子通过伪造人脸图像和视频实施电信诈骗
, 操纵舆论, 传播淫秽等目的, 如何从海量数据中高效准确地检测出伪造人脸成为研究焦点 …