mlsanityData to 7 Oct 2026

DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning

arXiv 2109.02874 ↗First posted 7 Sep 2021cs.CV cs.IRcited by 1

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Abstract (arXiv)

The rapid advancement in deep learning makes the differentiation of authentic and manipulated facial images and video clips unprecedentedly harder. The underlying technology of manipulating facial appearances through deep generative approaches, enunciated as DeepFake that have emerged recently by promoting a vast number of malicious face manipulation applications. Subsequently, the need of other sort of techniques that can assess the integrity of digital visual content is indisputable to reduce the impact of the creations of DeepFake. A large body of research that are performed on DeepFake creation and detection create a scope of pushing each other beyond the current status. This study presents challenges, research trends, and directions related to DeepFake creation and detection techniques by reviewing the notable research in the DeepFake domain to facilitate the development of more robust approaches that could deal with the more advance DeepFake in the future.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 72 of its 107 references that are in the map. First posted on arXiv 7 Sep 2021.

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72 of 107 references.

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  1. 2022GAN-generated Faces Detection: A Survey and New Perspectivescited by 29