Deepfake detection accuracy declining

paper

arXiv·arxiv.org/abs/2004.11138

Authors

Mirsky, Yisroel·Lee, Wenke

Credibility Rating

3/5

Good(3)

Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.

Rating inherited from publication venue: arXiv

Survey examining deepfake creation and detection methods, documenting declining detection accuracy as generative AI advances—critical for understanding adversarial threats to AI safety and the arms race between detection and generation capabilities.

Paper Details

Citations

41 influential

Year

2020

Methodology

dissertation

arXiv:2004.11138 DOI:10.31979/etd.92r7-ddvu Semantic Scholar

Metadata

arxiv preprintanalysis

Summary

A survey exploring the creation and detection of deepfakes, examining technological advancements, current trends, and potential threats in generative AI technologies.

Key Points

•Deepfakes use advanced neural networks to generate highly realistic synthetic media
•Technologies can be used for both creative and malicious purposes
•Rapid technological advancement makes detecting fake content increasingly challenging

Review

The paper provides a comprehensive overview of deepfake technologies, focusing on how artificial neural networks can generate highly believable synthetic media, particularly involving human faces and bodies. The authors explore the technological progression of deepfakes from 2017 to 2020, documenting the rapid advancement in generative deep learning algorithms that can manipulate, replace, and synthesize human imagery with increasing realism. The research highlights both creative and malicious potential of deepfake technologies, examining various approaches like facial reenactment, face swapping, and identity manipulation. By systematically reviewing different neural network architectures and techniques, the paper reveals the sophisticated methods used to generate synthetic media, while also emphasizing the significant ethical and security risks associated with these technologies, such as potential misuse for misinformation, impersonation, and social engineering.

Cited by 2 pages

Page	Type	Quality
Authentication Collapse	Risk	57.0
AI-Driven Legal Evidence Crisis	Risk	43.0

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[2004.11138] The Creation and Detection of Deepfakes: A Survey 
 
 
 
 
 
 
 
 
 
 
 

 
 
 

 
 
 
 
 
 
 The Creation and Detection of Deepfakes: A Survey

 
 
 Yisroel Mirsky
 
 yisroel@gatech.edu 
 
 yisroel@post.bgu.ac.il 
 
 Georgia Institute of Technology 756 W Peachtree St NW Atlanta Georgia 30308 
 
 Ben-Gurion University P.O.B. 653 Beer-Sheva Israel 8410501 
 
  and  
 Wenke Lee
 
 wenke@cc.gatech.edu 
 
 Georgia Institute of Technology 756 W Peachtree St NW Atlanta Georgia 30308 
 
 
 (2020) 

 
 Abstract.

 Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake. In 2018, it was discovered how easy it is to use this technology for unethical and malicious applications, such as the spread of misinformation, impersonation of political leaders, and the defamation of innocent individuals. Since then, these ‘deepfakes’ have advanced significantly.

 In this paper, we explore the creation and detection of deepfakes an provide an in-depth view how these architectures work. The purpose of this survey is to provide the reader with a deeper understanding of (1) how deepfakes are created and detected, (2) the current trends and advancements in this domain, (3) the shortcomings of the current defense solutions, and (4) the areas which require further research and attention.

 
 Deepfake, Deep fake, reenactment, replacement, face swap, generative AI, social engineering, impersonation
 
 † † copyright: acmcopyright † † journalyear: 2020 † † doi: XX.XXXX/XXXXXXX.XXXXXXX † † journal: CSUR † † ccs: Security and privacy Social engineering attacks † † ccs: Computing methodologies Machine learning † † ccs: Security and privacy Human and societal aspects of security and privacy 
 
 
 1. Introduction

 
 A deepfake is content, generated by an artificial intelligence, that is authentic in the eyes of a human being. The word deepfake is a combination of the words ‘deep learning’ and ‘fake’ and primarily relates to content generated by an artificial neural network, a branch of machine learning.

 
 
 The most common form of deepfakes involve the generation and manipulation of human imagery. This technology has creative and productive applications. For example, realistic video dubbing of foreign films, 1 1 1 https://variety.com/2019/biz/news/
 ai-dubbing-david-beckham-multilingual-1203309213/ education though the reanimation of historical figures (Lee, 2019 ) , and virtually trying on clothes while shopping. 2 2 2 https://www.forbes.com/sites/forbestechcouncil/2019/05/21/gans-and-deepfakes-could-revolutionize-the-fashion-industry/ There are also numerous online communities devoted to creating deepfake memes for entertainment, 3 3 3 https://www.reddit.com/r/SFWdeepfakes/ such as music videos portraying the face of actor Nicolas Cage.

 
 
 However, despite the positive applications of deepfakes, the technology is infamous for its unethical and malicious aspects.
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