Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System
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arXiv
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| Format: | Preprint |
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2025
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| author | Ali, Hashim Subramani, Surya Bollinani, Lekha Adupa, Nithin Sai El-Loh, Sali Malik, Hafiz |
| author_facet | Ali, Hashim Subramani, Surya Bollinani, Lekha Adupa, Nithin Sai El-Loh, Sali Malik, Hafiz |
| contents | The SAFE Challenge evaluates synthetic speech detection across three tasks: unmodified audio, processed audio with compression artifacts, and laundered audio designed to evade detection. We systematically explore self-supervised learning (SSL) front-ends, training data compositions, and audio length configurations for robust deepfake detection. Our AASIST-based approach incorporates WavLM large frontend with RawBoost augmentation, trained on a multilingual dataset of 256,600 samples spanning 9 languages and over 70 TTS systems from CodecFake, MLAAD v5, SpoofCeleb, Famous Figures, and MAILABS. Through extensive experimentation with different SSL front-ends, three training data versions, and two audio lengths, we achieved second place in both Task 1 (unmodified audio detection) and Task 3 (laundered audio detection), demonstrating strong generalization and robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20983 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System Ali, Hashim Subramani, Surya Bollinani, Lekha Adupa, Nithin Sai El-Loh, Sali Malik, Hafiz Audio and Speech Processing Machine Learning The SAFE Challenge evaluates synthetic speech detection across three tasks: unmodified audio, processed audio with compression artifacts, and laundered audio designed to evade detection. We systematically explore self-supervised learning (SSL) front-ends, training data compositions, and audio length configurations for robust deepfake detection. Our AASIST-based approach incorporates WavLM large frontend with RawBoost augmentation, trained on a multilingual dataset of 256,600 samples spanning 9 languages and over 70 TTS systems from CodecFake, MLAAD v5, SpoofCeleb, Famous Figures, and MAILABS. Through extensive experimentation with different SSL front-ends, three training data versions, and two audio lengths, we achieved second place in both Task 1 (unmodified audio detection) and Task 3 (laundered audio detection), demonstrating strong generalization and robustness. |
| title | Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System |
| topic | Audio and Speech Processing Machine Learning |
| url | https://arxiv.org/abs/2508.20983 |