Trident of Poseidon: A Generalized Approach for Detecting Deepfake Voices
Authors: 
Thien-Phuc Doan, Hung Dinh-Xuan, Taewon Ryu, Inho Kim, Woongjae Lee, Kihun Hong, Souhwan JungAuthors Info & Claims
Thien-Phuc Doan, Hung Dinh-Xuan, Taewon Ryu, Inho Kim, Woongjae Lee, Kihun Hong, Souhwan JungAuthors Info & ClaimsPages 2222 - 2235
Abstract
Deepfakes, an increasingly prevalent form of information attack, pose serious threats to security and privacy. Deepfake voice attacks, in particular, have the potential to cause widespread disruption, creating an urgent need for an effective detection system. In this research, we propose the Trident of Poseidon - a novel set of triad training strategies aimed at enhancing the generalizability of deepfake voice detection models. Our solution comprises three key components: (1) Supervised Contrastive Learning, (2) Hard Negative Mining by Audio Re-synthesizing, and (3) Effective Proactive Batch Sampling. Together, these enable the model to learn more robust features. Our extensive experiments demonstrate that our approach outperforms existing methods in both in-domain and out-of-domain testing scenarios, making significant strides toward securing digital media against deepfake voice attacks.
Furthermore, we conducted a deeper analysis to explore whether deepfake voices can be categorized into families. By identifying the factors that contribute to the formation of a deepfake voice family, we can better organize a deepfake voice corpus, thereby reducing the effort needed to combat the arms race challenge. Finally, to promote practical utility and community-wide adoption, we have made our solution publicly available as a web application available on deepfake.aisrc.technology, where users can utilize this tool to test for potential deepfake voices.
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- Trident of Poseidon: A Generalized Approach for Detecting Deepfake Voices
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December 2024
5188 pages
ISBN:9798400706363
DOI:10.1145/3658644
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- Bo Luo,
- Xiaojing Liao,
- Jun Xu,
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- This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2024-2020-0-01602) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation)
- This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.RS-2023-00230337, Advanced and Proactive AI Platform Research and Development Against Malicious Deepfakes)
- This work was supported by Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No.RS-2023-00263037, Robust deepfake audio detection development against adversarial attacks)
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