论文标题
关于子公司信息在重播攻击检测中的作用的研究
A study on the role of subsidiary information in replay attack spoofing detection
论文作者
论文摘要
在这项研究中，我们分析了各种子公司信息在进行重播攻击检测中的作用：``房间大小''，``回响''，``扬声器to-asv距离，''攻击者to-to-stopspeaker距离''和``重播设备质量''。作为分析子公司信息的一种手段，我们使用两个框架来减去或包括从深神经网络中提取的代码的子公司信息。为了减法，我们使用一个对抗过程框架，该框架使代码与子公司信息的基础向量正交。此外，我们利用多任务学习框架将附属信息包括在代码中。所有实验均使用提供的元数据使用ASVSPOOF 2019物理访问方案进行。通过对两种方法的结果分析，我们得出结论，当对深神经网络进行二进制分类培训时，各种类别的子公司信息在代码中的存在不足。通过多任务学习框架明确包含各种类别的子公司信息，可以帮助提高封闭情况下的性能。
In this study, we analyze the role of various categories of subsidiary information in conducting replay attack spoofing detection: `Room Size', `Reverberation', `Speaker-to-ASV distance, `Attacker-to-Speaker distance', and `Replay Device Quality'. As a means of analyzing subsidiary information, we use two frameworks to either subtract or include a category of subsidiary information to the code extracted from a deep neural network. For subtraction, we utilize an adversarial process framework which makes the code orthogonal to the basis vectors of the subsidiary information. For addition, we utilize the multi-task learning framework to include subsidiary information to the code. All experiments are conducted using the ASVspoof 2019 physical access scenario with the provided meta data. Through the analysis of the result of the two approaches, we conclude that various categories of subsidiary information does not reside enough in the code when the deep neural network is trained for binary classification. Explicitly including various categories of subsidiary information through the multi-task learning framework can help improve performance in closed set condition.
