Abstract
Recent advances in speech synthesis and voice conversion have greatly improved the naturalness and authenticity of generated audio. Meanwhile, evolving encoding, compression, and transmission mechanisms on social media platforms further obscure deepfake artifacts. These factors complicate reliable detection in real-world environments, underscoring the need for representative evaluation benchmarks. To this end, we introduce ML-ITW (Multilingual In-The-Wild), a multilingual dataset covering 14 languages, seven major platforms, and 180 public figures, totaling 28.39 hours of audio. We evaluate three detection paradigms: end-to-end neural models, self-supervised feature-based (SSL) methods, and audio large language models (Audio LLMs). Experimental results reveal significant performance degradation across diverse languages and real-world acoustic conditions, highlighting the limited generalization ability of existing detectors in practical scenarios. The ML-ITW dataset is publicly available.
Abstract (translated)
近期在语音合成和声音转换领域的进展大大提升了生成音频的自然度和真实性。与此同时，社交媒体平台上的编码、压缩和传输机制也在不断发展，这进一步模糊了深度伪造（deepfake）的痕迹。这些因素使得在现实环境中可靠地检测到深度伪造变得更加复杂，强调了需要具有代表性的评估基准的重要性。 为此，我们引入了ML-ITW（Multilingual In-The-Wild），这是一个涵盖14种语言、七个主要平台和180位公众人物的多语种数据集，总共有28.39小时的音频。我们在三个检测范式上进行了评估：端到端神经模型、自监督特征方法（SSL）以及音频大型语言模型（Audio LLMs）。实验结果表明，在多种语言及现实世界声学条件下，现有检测器的表现显著下降，这突显了它们在实际场景中的泛化能力有限。ML-ITW数据集现已公开提供。
URL
https://arxiv.org/abs/2603.05852
