Title: Discrete optimal transport is a strong audio adversarial attack Show Chinese title

Title: 离散最优传输是一种强大的音频对抗攻击

Abstract: In this paper, we show that discrete optimal transport (DOT) is an effective black-box adversarial attack against modern audio anti-spoofing countermeasures (CMs). Our attack operates as a post-processing, distribution-alignment step: frame-level WavLM embeddings of generated speech are aligned to an unpaired bona fide pool via entropic OT and a top-$k$ barycentric projection, then decoded with a neural vocoder. Evaluated on ASVspoof2019 and ASVspoof5 with AASIST baselines, DOT yields consistently high equal error rate (EER) across datasets and remains competitive after CM fine-tuning, outperforming several conventional attacks in cross-dataset transfer. Ablation analysis highlights the practical impact of vocoder overlap. Results indicate that distribution-level alignment is a powerful and stable attack surface for deployed CMs. Show Chinese abstract

Abstract: 在本文中，我们表明离散最优传输（DOT）是对现代音频防欺骗对策（CMs）的有效黑盒对抗攻击。 我们的攻击作为一个后处理、分布对齐步骤运行：通过熵最优传输和一个 top-$k$重心投影，将生成语音的帧级 WavLM 嵌入对齐到一个无配对的真品池，然后通过神经声码器解码。 在 ASVspoof2019 和 ASVspoof5 上使用 AASIST 基线进行评估，DOT 在不同数据集上始终表现出较高的等错误率（EER），并且在 CM 微调后仍具有竞争力，在跨数据集迁移中优于几种传统攻击。 消融分析突出了声码器重叠的实际影响。 结果表明，分布级对齐是部署的 CMs 的强大且稳定的攻击面。