AI 中文总结
该研究针对音频深度伪造检测器跨域性能下降问题，提出音频锚定融合多比率DiT重建残差的方法，在ASVspoof 5和ITW数据集上取得优于基准的跨域检测性能。
AI 中文摘要
音频深度伪造检测器在生成器、语料库或录音条件发生变化时往往性能下降。我们使用仅在真实语音上训练的扩散Transformer（DiT）作为冻结重建探针，在0.5、0.75和0.9三种掩码比率下的重建结果会产生明确的多比率残差图。由于这些残差具有域敏感性，我们的音频锚定检测器将投影后的冻结WavLM听觉表示无门控衰减地传入融合和，并将残差仅用作标量门控加法校正。预指定的种子42运行在ASVspoof 5评估集上获得6.5442%的等错误率（EER）、0.18456的最小检测成本函数（min-DCF），在ITW完整集上获得13.8372%的EER、0.36921的min-DCF；三次种子运行的平均值为6.8885（0.3308）%和15.3328（2.0719）%。后者在两种监督设置下均低于单独优化的WavLM-ResNet18基准。辅助监督将动态竞争融合的平均ITW EER从18.4007%提升至25.2968%，但会降低所有三次种子运行的性能。结果表明重建残差是互补证据，为ASVspoof 5到ITW的迁移提供了非竞争听觉路径的动机，且未声称锚定本身的组件级因果消融。
英文摘要
Audio deepfake detectors often degrade when generators, corpora, or recording conditions change. We use a Diffusion Transformer (DiT), trained only on bona fide speech, as a frozen reconstruction probe. Reconstructions at masking ratios 0.5, 0.75, and 0.9 yield explicit multi-ratio residual maps. Because these residuals are domain sensitive, our audio-anchored detector passes the projected frozen-WavLM auditory representation into the fusion sum without gate-based attenuation and uses residuals only as a scalar-gated additive correction. The pre-specified seed-42 run obtains 6.5442% EER / 0.18456 min-DCF on ASVspoof 5 Eval and 13.8372% / 0.36921 on ITW Full; three-seed means are 6.8885 (0.3308)% and 15.3328 (2.0719)%. The latter is below a separately optimized WavLM-ResNet18 reference under both supervision settings. Auxiliary supervision raises dynamic competitive fusion from 18.4007% to 25.2968% mean ITW EER, worsening all three seeds. The results support reconstruction residuals as complementary evidence and motivate a non-competitive auditory path for ASVspoof 5-to-ITW transfer, without claiming a componentwise causal ablation of anchoring alone.
Comments10 pages, 5 figures. Submitted to speech security conference. This work proposes a cross-domain audio deepfake detection framework based on bona-fide trained DiT multi-ratio reconstruction residuals and audio-anchored additive fusion, evaluated on ASVspoof 5 and real-world ITW datasets
