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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.09568 |
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| _version_ | 1866917529837895680 |
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| author | Luong, Hieu-Thi Liu, Xuechen Kukanov, Ivan Chai, Zheng Xin Lee, Kong Aik |
| author_facet | Luong, Hieu-Thi Liu, Xuechen Kukanov, Ivan Chai, Zheng Xin Lee, Kong Aik |
| contents | RADAR Challenge 2026 is an APSIPA Grand Challenge on Robust Audio Deepfake Recognition under Media Transformations, designed to simulate realistic media conditions in real-world audio distribution pipelines, including compression, resampling, noise, and reverberation. It consists of two phases: an English development phase with labeled data for analysis and paper writing, and a multilingual evaluation phase containing more than 100,000 utterances in English, Singapore English, Mandarin Chinese, Taiwanese Mandarin, Japanese, and Vietnamese. Systems are evaluated using equal error rate (EER) for binary real/fake classification. This paper describes the challenge task, the construction of the data set, the evaluation protocol, and the overall results. During the challenge, 33 teams submitted to the development phase and 22 teams submitted to the final evaluation phase. The reported results highlight the remaining challenges of robust audio deepfake detection under multilingual and media-transformed conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09568 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RADAR Challenge 2026: Robust Audio Deepfake Recognition under Media Transformations Luong, Hieu-Thi Liu, Xuechen Kukanov, Ivan Chai, Zheng Xin Lee, Kong Aik Audio and Speech Processing RADAR Challenge 2026 is an APSIPA Grand Challenge on Robust Audio Deepfake Recognition under Media Transformations, designed to simulate realistic media conditions in real-world audio distribution pipelines, including compression, resampling, noise, and reverberation. It consists of two phases: an English development phase with labeled data for analysis and paper writing, and a multilingual evaluation phase containing more than 100,000 utterances in English, Singapore English, Mandarin Chinese, Taiwanese Mandarin, Japanese, and Vietnamese. Systems are evaluated using equal error rate (EER) for binary real/fake classification. This paper describes the challenge task, the construction of the data set, the evaluation protocol, and the overall results. During the challenge, 33 teams submitted to the development phase and 22 teams submitted to the final evaluation phase. The reported results highlight the remaining challenges of robust audio deepfake detection under multilingual and media-transformed conditions. |
| title | RADAR Challenge 2026: Robust Audio Deepfake Recognition under Media Transformations |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2605.09568 |