Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System
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.
Keywords
Cite
@article{arxiv.2508.20983,
title = {Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System},
author = {Hashim Ali and Surya Subramani and Lekha Bollinani and Nithin Sai Adupa and Sali El-Loh and Hafiz Malik},
journal= {arXiv preprint arXiv:2508.20983},
year = {2025}
}
Comments
Accepted @ IEEE ASRU 2025