{"type":"ScholarlyArticle","title":"Discriminative Feature Decoupling Enhancement for Speech Forgery Detection","authors":[{"name":"Yijun Bei","slug":"yijun-bei","given":"Yijun","family":"Bei","affiliation":"School of Software Technology , Zhejiang University"},{"name":"Xing Zhou","slug":"xing-zhou","given":"Xing","family":"Zhou","affiliation":"School of Software Technology , Zhejiang University; State Key Laboratory of Blockchain and Security , Zhejiang University; Hangzhou High-Tech Zone (Binjiang ) Institute of Blockchain and Data Security"},{"name":"Erteng Liu","slug":"erteng-liu","given":"Erteng","family":"Liu","affiliation":"School of Software Technology , Zhejiang University; State Key Laboratory of Blockchain and Security , Zhejiang University; Hangzhou High-Tech Zone (Binjiang ) Institute of Blockchain and Data Security"},{"name":"Yang Gao","slug":"yang-gao","given":"Yang","family":"Gao","affiliation":"School of Software Technology , Zhejiang University; Hangzhou High-Tech Zone (Binjiang ) Institute of Blockchain and Data Security"},{"name":"Sen Lin","slug":"sen-lin","given":"Sen","family":"Lin","affiliation":"Ningbo Donghai Group Co., Ltd."},{"name":"Kewei Gao","slug":"kewei-gao","given":"Kewei","family":"Gao","affiliation":"School of Software Technology , Zhejiang University; State Key Laboratory of Blockchain and Security , Zhejiang University; Hangzhou High-Tech Zone (Binjiang ) Institute of Blockchain and Data Security"},{"name":"Zunlei Feng","slug":"zunlei-feng","given":"Zunlei","family":"Feng","affiliation":"School of Software Technology , Zhejiang University; State Key Laboratory of Blockchain and Security , Zhejiang University; Hangzhou High-Tech Zone (Binjiang ) Institute of Blockchain and Data Security"}],"abstract":"The emergence of AIGC has brought attention to the issue of generating realistic deceptive content. While AIGC has the potential to revolutionize content creation, it also facilitates criminal activities. Specifically, the manipulation of speech has been exploited in tele-fraud and financial fraud schemes, posing a significant threat to societal security. Current deep learning-based methods for detecting forged speech extract mixed features from the original speech, which often contain redundant information. Moreover, these methods fail to consider the distinct characteristics of human voice-specific features and the diversity of background environmental sounds.\r\nThis paper introduces a framework called Discriminative fEature dEcoupling enhanceMent (DEEM) for detecting speech forgery. Initially, the framework decouples the original speech into human voice features and background sound features. Subsequently, DEEM enhances voice-specific features through temporal dimension aggregation and improves continuity-related features in the background sound map via spectral-dimension aggregation. By employing the decoupling enhancement features, extensive experiments demonstrate that DEEM achieves an accuracy improvement of over 5% on FoR dataset compared to the state-of-the-art methods.","doi":"10.24963/ijcai.2024/38","url":"https://ijcai.org/proceedings/2024/38","pdf":"https://ijcai.org/proceedings/2024/0038.pdf","markdown":"https://ijcai.org/proceedings/2024/38.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/38","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI Ethics, Trust, Fairness","track":"Main Track","number":38,"pages":{"first":340,"last":348},"keywords":["AI Ethics, Trust, Fairness: ETF: AI and law, governance, regulation","AI Ethics, Trust, Fairness: ETF: Safety and robustness","AI Ethics, Trust, Fairness: ETF: Societal impact of AI","AI Ethics, Trust, Fairness: ETF: Fairness and diversity"],"videos":[],"references":[{"position":1,"raw":"[Afchar et al., 2018] Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. 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