The creation or manipulation of facial appearance through deep generative approaches,
known as DeepFake, have achieved significant progress and promoted a wide range of …

Despite significant advancements of deep learning-based forgery detectors for
distinguishing manipulated deepfake images, most detection approaches suffer from …

The existing face forgery algorithms have achieved remarkable progress in how to generate
reasonable facial images and can even successfully deceive human beings. Considering …

Recent advancements in deep neural networks especially GAN (Generative Adversarial
Network) have resulted in the creation of more realistic deepfake media. This technology …

The deepfake technology is conveniently abused with the low technology threshold, which
may bring the huge social security risks. As GAN‐based synthesis technology is becoming …

With the emergence of deep learning, generating forged images or videos has become
much easier in recent years. Face forgery detection, as a way to detect forgery, is an …

Image manipulation has become a common problem due to the tremendous growth of digital
tools and applications. Several image forgeries can uneventfully eradicate the original …

Rapid advancements in the field of computer vision and AI have enabled the creation of
synthesized images and videos known as deepfakes. Deepfakes are used as a source of …

Deep neural networks (DNNs) have extensively promoted data generation development; the
quality of these generated content has achieved an impressive new level. Therefore …

The accelerated growth in synthetic visual media generation and manipulation has now
reached the point of raising significant concerns and posing enormous intimidations towards …