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This paper proposes a supervised learning framework for detecting deepfake audio using a generative adversarial network (GAN) with spatial\u2013temporal feature learning. The audio is first de-noised using a Wiener filter, and then key pitch and speech features, including Mel-frequency cepstral coefficients (MFCCs), pitch entropy, harmonic-to-noise ratio (HNR) and glottal flow, are extracted. These features are processed by a one-dimensional dilated spatial\u2013temporal attention-based bidirectional gated recurring units (ODDST-CBG) to capture nuanced patterns. A Wasserstein GAN (W-GAN) discriminator is used for final classification. Experimental results on a large-scale fake versus real speech dataset demonstrate high performance, achieving 99.98% accuracy, 99.08% precision, 97.08% sensitivity and a 97.08% F1-score, surpassing existing baseline methods.<\/jats:p>","DOI":"10.1142\/s0218194025500676","type":"journal-article","created":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T02:51:12Z","timestamp":1759546272000},"page":"363-379","source":"Crossref","is-referenced-by-count":0,"title":["Deepfake Audio Detection Using Generative Adversarial Network-Based Spatial\u2013Temporal Feature Learning"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9574-7065","authenticated-orcid":false,"given":"Vivek","family":"Ranjan","sequence":"first","affiliation":[{"name":"Department of Information Technology and Computer Applications, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh 273016, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2212-1725","authenticated-orcid":false,"given":"Dayashankar","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Computer Applications, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh 273016, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,11,29]]},"reference":[{"key":"S0218194025500676BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3344653"},{"key":"S0218194025500676BIB002","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging9060122"},{"key":"S0218194025500676BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3342107"},{"key":"S0218194025500676BIB004","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3324724"},{"key":"S0218194025500676BIB005","doi-asserted-by":"publisher","DOI":"10.3390\/app12083926"},{"key":"S0218194025500676BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/URTC60662.2023.10534969"},{"key":"S0218194025500676BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123941"},{"key":"S0218194025500676BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/IS262782.2024.10704095"},{"key":"S0218194025500676BIB009","doi-asserted-by":"publisher","DOI":"10.1109\/AIIoT58432.2024.10574576"},{"key":"S0218194025500676BIB010","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3506973"},{"key":"S0218194025500676BIB011","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-19819-z"},{"key":"S0218194025500676BIB012","doi-asserted-by":"publisher","DOI":"10.3390\/electronics13173433"},{"key":"S0218194025500676BIB013","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO63174.2024.10715076"},{"key":"S0218194025500676BIB014","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.129256"},{"key":"S0218194025500676BIB015","doi-asserted-by":"publisher","DOI":"10.3390\/computers13100256"},{"issue":"3","key":"S0218194025500676BIB016","first-page":"1","volume":"57","author":"Wang T.","year":"2024","journal-title":"ACM Comput. 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