SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908251848704000 |
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| author | Le, Binh M. Kim, Jiwon Woo, Simon S. Moore, Kristen Abuadbba, Alsharif Tariq, Shahroz |
| author_facet | Le, Binh M. Kim, Jiwon Woo, Simon S. Moore, Kristen Abuadbba, Alsharif Tariq, Shahroz |
| contents | Deepfakes have rapidly emerged as a serious threat to society due to their ease of creation and dissemination, triggering the accelerated development of detection technologies. However, many existing detectors rely on labgenerated datasets for validation, which may not prepare them for novel, real-world deepfakes. This paper extensively reviews and analyzes state-of-the-art deepfake detectors, evaluating them against several critical criteria. These criteria categorize detectors into 4 high-level groups and 13 finegrained sub-groups, aligned with a unified conceptual framework we propose. This classification offers practical insights into the factors affecting detector efficacy. We evaluate the generalizability of 16 leading detectors across comprehensive attack scenarios, including black-box, white-box, and graybox settings. Our systematized analysis and experiments provide a deeper understanding of deepfake detectors and their generalizability, paving the way for future research and the development of more proactive defenses against deepfakes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04364 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework Le, Binh M. Kim, Jiwon Woo, Simon S. Moore, Kristen Abuadbba, Alsharif Tariq, Shahroz Computer Vision and Pattern Recognition Cryptography and Security Machine Learning Deepfakes have rapidly emerged as a serious threat to society due to their ease of creation and dissemination, triggering the accelerated development of detection technologies. However, many existing detectors rely on labgenerated datasets for validation, which may not prepare them for novel, real-world deepfakes. This paper extensively reviews and analyzes state-of-the-art deepfake detectors, evaluating them against several critical criteria. These criteria categorize detectors into 4 high-level groups and 13 finegrained sub-groups, aligned with a unified conceptual framework we propose. This classification offers practical insights into the factors affecting detector efficacy. We evaluate the generalizability of 16 leading detectors across comprehensive attack scenarios, including black-box, white-box, and graybox settings. Our systematized analysis and experiments provide a deeper understanding of deepfake detectors and their generalizability, paving the way for future research and the development of more proactive defenses against deepfakes. |
| title | SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework |
| topic | Computer Vision and Pattern Recognition Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2401.04364 |