Paper
How Well Do Current Speech Deepfake Detection Methods Generalize to the Real World?
Authors
Daixian Li, Jun Xue, Yanzhen Ren, Zhuolin Yi, Yihuan Huang, Guanxiang Feng, Yi Chai
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.
Metadata
Related papers
Gen-Searcher: Reinforcing Agentic Search for Image Generation
Kaituo Feng, Manyuan Zhang, Shuang Chen, Yunlong Lin, Kaixuan Fan, Yilei Jian... • 2026-03-30
On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers
Omer Dahary, Benaya Koren, Daniel Garibi, Daniel Cohen-Or • 2026-03-30
Graphilosophy: Graph-Based Digital Humanities Computing with The Four Books
Minh-Thu Do, Quynh-Chau Le-Tran, Duc-Duy Nguyen-Mai, Thien-Trang Nguyen, Khan... • 2026-03-30
ParaSpeechCLAP: A Dual-Encoder Speech-Text Model for Rich Stylistic Language-Audio Pretraining
Anuj Diwan, Eunsol Choi, David Harwath • 2026-03-30
RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems
Oliver Aleksander Larsen, Mahyar T. Moghaddam • 2026-03-30
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.05852v1</id>\n <title>How Well Do Current Speech Deepfake Detection Methods Generalize to the Real World?</title>\n <updated>2026-03-06T03:18:16Z</updated>\n <link href='https://arxiv.org/abs/2603.05852v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.05852v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.SD'/>\n <published>2026-03-06T03:18:16Z</published>\n <arxiv:comment>Submitted to Interspeech 2026</arxiv:comment>\n <arxiv:primary_category term='cs.SD'/>\n <author>\n <name>Daixian Li</name>\n </author>\n <author>\n <name>Jun Xue</name>\n </author>\n <author>\n <name>Yanzhen Ren</name>\n </author>\n <author>\n <name>Zhuolin Yi</name>\n </author>\n <author>\n <name>Yihuan Huang</name>\n </author>\n <author>\n <name>Guanxiang Feng</name>\n </author>\n <author>\n <name>Yi Chai</name>\n </author>\n </entry>"
}