AdvSV：一种用于说话人验证的空中对抗攻击数据集
AdvSV: An Over-the-Air Adversarial Attack Dataset for Speaker Verification
AI总结：
本文提出AdvSV，一个基于Voxceleb1构建的开源空中对抗攻击数据集，用于说话人验证研究，并提供检测基线以促进可复现研究。
摘要：
众所周知，深度神经网络容易受到对抗攻击。尽管基于深度神经网络构建的自动说话人验证（ASV）在受控场景中表现出鲁棒性能，但许多研究证实ASV容易受到对抗攻击。缺乏标准数据集是进一步研究的瓶颈，尤其是可复现研究。在本研究中，我们开发了一个用于说话人验证研究的开源对抗攻击数据集。作为第一步，我们聚焦于空中（over-the-air）攻击。空中对抗攻击涉及扰动生成算法、扬声器、麦克风和声学环境。录音配置的变化使得复现先前研究非常具有挑战性。AdvSV数据集以Voxceleb1验证测试集为基础构建。该数据集采用具有代表性的ASV模型，使其遭受对抗攻击，并录制对抗样本以模拟空中攻击设置。该数据集的范围可以轻松扩展以包含更多类型的对抗攻击。该数据集将在CC BY-SA 4.0许可下公开发布。此外，我们还提供了一个检测基线以支持可复现研究。
英文摘要：
It is known that deep neural networks are vulnerable to adversarial attacks. Although Automatic Speaker Verification (ASV) built on top of deep neural networks exhibits robust performance in controlled scenarios, many studies confirm that ASV is vulnerable to adversarial attacks. The lack of a standard dataset is a bottleneck for further research, especially reproducible research. In this study, we developed an open-source adversarial attack dataset for speaker verification research. As an initial step, we focused on the over-the-air attack. An over-the-air adversarial attack involves a perturbation generation algorithm, a loudspeaker, a microphone, and an acoustic environment. The variations in the recording configurations make it very challenging to reproduce previous research. The AdvSV dataset is constructed using the Voxceleb1 Verification test set as its foundation. This dataset employs representative ASV models subjected to adversarial attacks and records adversarial samples to simulate over-the-air attack settings. The scope of the dataset can be easily extended to include more types of adversarial attacks. The dataset will be released to the public under the CC BY-SA 4.0. In addition, we also provide a detection baseline for reproducible research.
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