提出可抵抗变速变调的AI音乐检测方法，提升实际应用鲁棒性。
Improved Robustness in AI-Generated Music Detection
- 通过对数频谱映射实现频域不变性，抗音频变速和变调攻击。
- 联合监督二分类与伪影定位，使检测准确率在变速下仍保持90%以上。
- 输出结果兼具判断与速度变化估计，适合需要可解释性的场景。
AI音乐生成器会留下由其架构决定的可预测频谱伪影。现有检测器在原始生成音频上几乎完美，但在简单的音频操作（如变速、变调）下性能急剧下降。本文提出一种频率缩放不变的检测流水线，从设计上防御此类攻击。方法通过基于对数-短时傅里叶变换（log-STFT）的频谱重映射，将音频转换至对数频率轴；结合单个可学习的互相关滤波器与最大池化，实现推理时的平移不变性。训练采用混合损失，同时监督二分类任务与伪影峰值定位，正则化边界权重。由于对变速的鲁棒性是设计内置特性，该检测器还具备可解释性：不仅能输出二元判断，还能估计出施加的变速因子。
原文摘要 · Abstract (English)
AI music generators leave predictable spectral artifacts determined by their architecture. Existing detectors exploit these artifacts with near-perfect accuracy on raw generated tracks, but their performance collapses under simple audio manipulations, such as speed modification or pitch shifting. We address this open robustness problem by introducing a frequency-scaling-invariant detection pipeline that aims to prevent this kind of attack by design. Our method maps audio onto a log-frequency axis via a log-STFT remapping. A single learned cross-correlation filter, combined with max-pooling, provides shift invariance at inference time. Training uses a hybrid loss that jointly supervises binary detection and artifact-peak localization, regularizing boundary weights. Because robustness to speed change is built in by design, the detector is also interpretable: it outputs both a binary decision and an estimate of the applied speed-change factor.
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