用因果分析找出音频分类器依赖的关键频率特征，可微调干扰分类结果。
I Guess That's Why They Call it the Blues: Causal Analysis for Audio Classifiers
- 基于因果推理识别音频分类的必要且充分频率特征
- 仅调整24万频点中1个，58%情况下改变分类结果
- 干扰极小且人耳难辨，适合研究模型脆弱性
音频分类器常依赖非音乐相关特征和虚假关联进行分类，易被操纵或误导。尽管导致误分类不难，但此前尚未明确其依赖的具体特征。本文提出一种新方法，利用因果推理识别对分类结果必要且充分的频域特征。我们实现了该算法工具FreqReX，并在多个标准基准数据集上验证。实验表明，仅修改24万频点中的一个，即可在58%的情况下改变模型输出，且干扰程度几乎不可听。这些结果证明因果分析有助于理解音频分类器的决策机制，并可有效操控其输出。
原文摘要 · Abstract (English)
It is well-known that audio classifiers often rely on non-musically relevant features and spurious correlations to classify audio. Hence audio classifiers are easy to manipulate or confuse, resulting in wrong classifications. While inducing a misclassification is not hard, until now the set of features that the classifiers rely on was not well understood. In this paper we introduce a new method that uses causal reasoning to discover features of the frequency space that are sufficient and necessary for a given classification. We describe an implementation of this algorithm in the tool FreqReX and provide experimental results on a number of standard benchmark datasets. Our experiments show that causally sufficient and necessary subsets allow us to manipulate the outputs of the models in a variety of ways by changing the input very slightly. Namely, a change to one out of 240,000 frequencies results in a change in classification 58% of the time, and the change can be so small that it is practically inaudible. These results show that causal analysis is useful for understanding the reasoning process of audio classifiers and can be used to successfully manipulate their outputs.
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