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Countering Malicious DeepFakes: Survey, Battleground, and Horizon
The creation or manipulation of facial appearance through deep generative approaches, known as DeepFake, have achieved significant progress and promoted a wide range of benign and malicious applications, e.g., visual effect assistance in movie and misinformation generation by faking famous persons....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9066404/ https://www.ncbi.nlm.nih.gov/pubmed/35528632 http://dx.doi.org/10.1007/s11263-022-01606-8 |
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author | Juefei-Xu, Felix Wang, Run Huang, Yihao Guo, Qing Ma, Lei Liu, Yang |
author_facet | Juefei-Xu, Felix Wang, Run Huang, Yihao Guo, Qing Ma, Lei Liu, Yang |
author_sort | Juefei-Xu, Felix |
collection | PubMed |
description | The creation or manipulation of facial appearance through deep generative approaches, known as DeepFake, have achieved significant progress and promoted a wide range of benign and malicious applications, e.g., visual effect assistance in movie and misinformation generation by faking famous persons. The evil side of this new technique poses another popular study, i.e., DeepFake detection aiming to identify the fake faces from the real ones. With the rapid development of the DeepFake-related studies in the community, both sides (i.e., DeepFake generation and detection) have formed the relationship of battleground, pushing the improvements of each other and inspiring new directions, e.g., the evasion of DeepFake detection. Nevertheless, the overview of such battleground and the new direction is unclear and neglected by recent surveys due to the rapid increase of related publications, limiting the in-depth understanding of the tendency and future works. To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of DeepFake detection, with more than 318 research papers carefully surveyed. We present the taxonomy of various DeepFake generation methods and the categorization of various DeepFake detection methods, and more importantly, we showcase the battleground between the two parties with detailed interactions between the adversaries (DeepFake generation) and the defenders (DeepFake detection). The battleground allows fresh perspective into the latest landscape of the DeepFake research and can provide valuable analysis towards the research challenges and opportunities as well as research trends and future directions. We also elaborately design interactive diagrams (http://www.xujuefei.com/dfsurvey) to allow researchers to explore their own interests on popular DeepFake generators or detectors. |
format | Online Article Text |
id | pubmed-9066404 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-90664042022-05-04 Countering Malicious DeepFakes: Survey, Battleground, and Horizon Juefei-Xu, Felix Wang, Run Huang, Yihao Guo, Qing Ma, Lei Liu, Yang Int J Comput Vis Article The creation or manipulation of facial appearance through deep generative approaches, known as DeepFake, have achieved significant progress and promoted a wide range of benign and malicious applications, e.g., visual effect assistance in movie and misinformation generation by faking famous persons. The evil side of this new technique poses another popular study, i.e., DeepFake detection aiming to identify the fake faces from the real ones. With the rapid development of the DeepFake-related studies in the community, both sides (i.e., DeepFake generation and detection) have formed the relationship of battleground, pushing the improvements of each other and inspiring new directions, e.g., the evasion of DeepFake detection. Nevertheless, the overview of such battleground and the new direction is unclear and neglected by recent surveys due to the rapid increase of related publications, limiting the in-depth understanding of the tendency and future works. To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of DeepFake detection, with more than 318 research papers carefully surveyed. We present the taxonomy of various DeepFake generation methods and the categorization of various DeepFake detection methods, and more importantly, we showcase the battleground between the two parties with detailed interactions between the adversaries (DeepFake generation) and the defenders (DeepFake detection). The battleground allows fresh perspective into the latest landscape of the DeepFake research and can provide valuable analysis towards the research challenges and opportunities as well as research trends and future directions. We also elaborately design interactive diagrams (http://www.xujuefei.com/dfsurvey) to allow researchers to explore their own interests on popular DeepFake generators or detectors. Springer US 2022-05-04 2022 /pmc/articles/PMC9066404/ /pubmed/35528632 http://dx.doi.org/10.1007/s11263-022-01606-8 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Juefei-Xu, Felix Wang, Run Huang, Yihao Guo, Qing Ma, Lei Liu, Yang Countering Malicious DeepFakes: Survey, Battleground, and Horizon |
title | Countering Malicious DeepFakes: Survey, Battleground, and Horizon |
title_full | Countering Malicious DeepFakes: Survey, Battleground, and Horizon |
title_fullStr | Countering Malicious DeepFakes: Survey, Battleground, and Horizon |
title_full_unstemmed | Countering Malicious DeepFakes: Survey, Battleground, and Horizon |
title_short | Countering Malicious DeepFakes: Survey, Battleground, and Horizon |
title_sort | countering malicious deepfakes: survey, battleground, and horizon |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9066404/ https://www.ncbi.nlm.nih.gov/pubmed/35528632 http://dx.doi.org/10.1007/s11263-022-01606-8 |
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