Deep Learning (DL) has been effectively utilized in various complicated challenges in
healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung …

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

Deepfake is utilized in synthetic media to generate fake visual and audio content based on a
person's existing media. The deepfake replaces a person's face and voice with fake media …

Due to the fast spread of data through digital media, individuals and societies must assess
the reliability of information. Deepfakes are not a novel idea but they are now a widespread …

Detecting forgery videos is highly desirable due to the abuse of deepfake. Existing detection
approaches contribute to exploring the specific artifacts in deepfake videos and fit well on …

Creating deepfakes has rapidly become easier and more accessible due to advancements
in hardware and computing. The harmful nature of deepfakes urges immediate action to …

Generative deep learning techniques have invaded the public discourse recently. Despite
the advantages, the applications to disinformation are concerning as the counter-measures …

Due to its high societal impact, deepfake detection is getting active attention in the computer
vision community. Most deepfake detection methods rely on identity, facial attributes, and …

Most deepfake detection methods focus on detecting spatial and/or spatio-temporal changes
in facial attributes and are centered around the binary classification task of detecting …

This study conducts research on deepfakes technology evolution and trends based on a
bibliometric analysis of the articles published on this topic along with six research questions …