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Why Do Facial Deepfake Detectors Fail?

Published: 02 September 2023 Publication History
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  • Abstract

    Recent rapid advancements in deepfake technology have allowed the creation of highly realistic fake media, such as video, image, and audio. These materials pose significant challenges to human authentication, such as impersonation, misinformation, or even a threat to national security. To keep pace with these rapid advancements, several deepfake detection algorithms have been proposed, leading to an ongoing arms race between deepfake creators and deepfake detectors. Nevertheless, these detectors are often unreliable and frequently fail to detect deepfakes. This study highlights the challenges they face in detecting deepfakes, including (1) the pre-processing pipeline of artifacts and (2) the fact that generators of new, unseen deepfake samples have not been considered when building the defense models. Our work sheds light on the need for further research and development in this field to create more robust and reliable detectors.

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    Cited By

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    • (2024) Information apocalypse or overblown fears—what AI mis‐ and disinformation is all about? Shifting away from technology toward human reactions Politics & Policy10.1111/polp.12617Online publication date: 6-Jun-2024
    • (2024)Responding to Deepfake Challenges in the United Kingdom: Legal and Technical Insights with RecommendationsCyberspace, Cyberterrorism and the International Security in the Fourth Industrial Revolution10.1007/978-3-031-50454-9_12(241-258)Online publication date: 19-Jan-2024
    • (2023)Improving Detection of DeepFakes through Facial Region Analysis in ImagesElectronics10.3390/electronics1301012613:1(126)Online publication date: 28-Dec-2023

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    1. Why Do Facial Deepfake Detectors Fail?

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      Published In

      cover image ACM Conferences
      WDC '23: Proceedings of the 2nd Workshop on Security Implications of Deepfakes and Cheapfakes
      July 2023
      37 pages
      ISBN:9798400702037
      DOI:10.1145/3595353
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      New York, NY, United States

      Publication History

      Published: 02 September 2023

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      Author Tags

      1. Adversarial noise
      2. Deepfake Detection
      3. Image manipulation
      4. Self-supervised learning

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      • Poster
      • Research
      • Refereed limited

      Funding Sources

      • Artificial Intelligence Innovation Hub
      • Advanced and Proactive AI Platform Research and Development Against Malicious Deepfakes
      • AI Graduate School Support Program at Sungkyunkwan University
      • Self-directed Multi-Modal Intelligence for solving unknown, open domain problems
      • Graduate School of Convergence Security at Sungkyunkwan University
      • AI Platform to Fully Adapt and Reflect Privacy-Policy Changes

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      ASIA CCS '23
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      Cited By

      View all
      • (2024) Information apocalypse or overblown fears—what AI mis‐ and disinformation is all about? Shifting away from technology toward human reactions Politics & Policy10.1111/polp.12617Online publication date: 6-Jun-2024
      • (2024)Responding to Deepfake Challenges in the United Kingdom: Legal and Technical Insights with RecommendationsCyberspace, Cyberterrorism and the International Security in the Fourth Industrial Revolution10.1007/978-3-031-50454-9_12(241-258)Online publication date: 19-Jan-2024
      • (2023)Improving Detection of DeepFakes through Facial Region Analysis in ImagesElectronics10.3390/electronics1301012613:1(126)Online publication date: 28-Dec-2023

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