Deepfakes

The term “deepfake” is derived from a combination of “deep learning”, a subset of artificial intelligence used to process and generate realistic content, and “fake”, highlighting the synthetic and deceptive nature of the media it produces. Deepfakes represent a category of synthetic media, encompassing videos, images, and audio recordings, that are artificially generated to produce highly convincing alterations of reality. These manipulations often involve the substitution of facial features, modification of vocal patterns, or the fabrication of entire scenes. The underlying technology relies on deep learning methodologies, notably Generative Adversarial Networks (GANs), to achieve this level of realism. Consequently, deepfakes are being deployed across a spectrum of applications, ranging from constructive to malevolent, thereby generating substantial ethical, security, and informational integrity challenges (Chesney & Citron, 2019).

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Publication Details

Journal
Kent Academic Repository (University of Kent)
Published
2026-04-01
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
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article

Deepfakes

Rao Faizan Ali, Jason R. C. Nurse
Kent Academic Repository (University of Kent)
Generative Adversarial Networks and Image Synthesis
article

Deepfakes

Rao Faizan Ali, Jason R. C. Nurse
article en

Abstract

The term “deepfake” is derived from a combination of “deep learning”, a subset of artificial intelligence used to process and generate realistic content, and “fake”, highlighting the synthetic and deceptive nature of the media it produces. Deepfakes represent a category of synthetic media, encompassing videos, images, and audio recordings, that are artificially generated to produce highly convincing alterations of reality. These manipulations often involve the substitution of facial features, modification of vocal patterns, or the fabrication of entire scenes. The underlying technology relies on deep learning methodologies, notably Generative Adversarial Networks (GANs), to achieve this level of realism. Consequently, deepfakes are being deployed across a spectrum of applications, ranging from constructive to malevolent, thereby generating substantial ethical, security, and informational integrity challenges (Chesney & Citron, 2019).

Kent Academic Repository (University of Kent)
Peace, Justice and strong institutions
Openalex Percentile: Top 4%
Generative Adversarial Networks and Image Synthesis
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