Malacopula：使用基于神经网络的广义Hammerstein模型的对抗性自动说话人验证攻击
Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein model
AI总结：
本研究提出基于神经网络的广义Hammerstein模型Malacopula，通过生成对抗性扰动提升语音欺骗攻击对ASV系统的效果，实验验证了其攻击效力及现有防御的局限，凸显了完善ASV对抗防御的必要性。
摘要：
我们提出了Malacopula，这是一种基于神经网络的广义Hammerstein模型，旨在向欺骗性语音样本中引入对抗性扰动，使其能更好地骗过自动说话人验证（ASV）系统。Malacopula通过非线性过程修改语音样本，提升了欺骗攻击的有效性。该模型由多条多项式函数并行分支及后续的线性时不变滤波器组成，其对抗优化过程用于最小化从欺骗语音与真实语音中提取的说话人嵌入之间的余弦距离。我们使用3种最新的ASV系统和ASVspoof 2019数据集开展了实验，结果表明Malacopula大幅提升了系统脆弱性。不过，该方法会降低语音质量，且在受控条件下攻击可被有效检测。这些发现凸显了识别新型脆弱性、设计防御措施以保护真实场景中的ASV系统免受对抗攻击的必要性。
英文摘要：
We present Malacopula, a neural-based generalised Hammerstein model designed to introduce adversarial perturbations to spoofed speech utterances so that they better deceive automatic speaker verification (ASV) systems. Using non-linear processes to modify speech utterances, Malacopula enhances the effectiveness of spoofing attacks. The model comprises parallel branches of polynomial functions followed by linear time-invariant filters. The adversarial optimisation procedure acts to minimise the cosine distance between speaker embeddings extracted from spoofed and bona fide utterances. Experiments, performed using three recent ASV systems and the ASVspoof 2019 dataset, show that Malacopula increases vulnerabilities by a substantial margin. However, speech quality is reduced and attacks can be detected effectively under controlled conditions. The findings emphasise the need to identify new vulnerabilities and design defences to protect ASV systems from adversarial attacks in the wild.
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