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arXiv · 2508.20983

Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System

Hashim Ali·Surya Subramani·Lekha Bollinani·Nithin Sai Adupa·Sali El-Loh·Hafiz Malik
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Abstract

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.

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Hashim Ali, Surya Subramani, Lekha Bollinani, Nithin Sai Adupa, Sali El-Loh, Hafiz Malik. 2025-08-28. Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System. https://arxiv.org/abs/2508.20983

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