Easy access to audio-visual content on social media, combined with the availability of
modern tools such as Tensorflow or Keras, and open-source trained models, along with …

When used maliciously, deepfake can pose detrimental implications to political and social
forces including reducing public trust in institutions, damaging the reputation of prominent …

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

In smart communities, social media allowed users easy access to multimedia content. With
recent advancements in computer vision and natural language processing, machine …

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 …

Recent years have seen a substantial increase in interest in deepfakes, a fast-developing
field at the nexus of artificial intelligence and multimedia. These artificial media creations …

Deep learning has been successfully applied to solve various complex problems ranging
from big data analytics to computer vision and human-level control. Deep learning advances …

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 …

A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of
a standardized, unified, comprehensive benchmark. This issue leads to unfair performance …

Deepfake refers to realistic, but fake images, sounds, and videos generated by articial
intelligence methods. Recent advances in deepfake generation make deepfake more …