SEAR: Spoofing Evidence-Grounded Audio Reasoning Benchmark for Audio Language Models
Audio language models (ALMs) are increasingly used for audio deepfake detection (ADD), yet existing benchmarks assess their verdicts or rationale plausibility without verifying the underlying acoustic evidence. To address this issue, we first introduce spoofing evidence-grounded audio reasoning (SEAR), a four-task AQA benchmark to evaluate ALM-based ADD through acoustic evidence identification and quantification, deepfake detection, and forensic rationale generation. We further propose a bona-fide-based acoustic evidence agent (BAEA), which equips a frozen ALM with controlled acoustic tools under \textsc{fixed} or \textsc{adaptive} evidence-acquisition policies. Experiments with six ALMs ...
| Authors | Rong Wan, Suliu Qin, Jiaxi Li et al. |
|---|---|
| Primary category | cs.SD |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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