论文标题
SOK：我们的ASRS中的故障：针对自动语音识别和说话者识别系统的攻击概述
SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems
论文作者
论文摘要
语音和说话者识别系统用于各种应用程序，从个人助理到电话监视和生物识别验证。通过神经网络中的准确性提高，这些系统的广泛部署已成为可能。像其他基于神经网络的系统一样，最近的研究表明，语音和说话者识别系统容易受到使用操纵输入的攻击。但是，正如我们在本文中所证明的那样，语音和说话者系统的端到端架构及其投入的性质使对它们的攻击和防御与图像空间中的攻击和防御大大不同。我们首先通过对该领域的现有研究进行系统化并提供分类法来证明这一点，并通过该分类法可以评估未来的工作。然后，我们通过实验证明对这些模型的攻击几乎普遍无法转移。这样一来，我们认为需要大量额外的工作才能在该领域提供足够的缓解。
Speech and speaker recognition systems are employed in a variety of applications, from personal assistants to telephony surveillance and biometric authentication. The wide deployment of these systems has been made possible by the improved accuracy in neural networks. Like other systems based on neural networks, recent research has demonstrated that speech and speaker recognition systems are vulnerable to attacks using manipulated inputs. However, as we demonstrate in this paper, the end-to-end architecture of speech and speaker systems and the nature of their inputs make attacks and defenses against them substantially different than those in the image space. We demonstrate this first by systematizing existing research in this space and providing a taxonomy through which the community can evaluate future work. We then demonstrate experimentally that attacks against these models almost universally fail to transfer. In so doing, we argue that substantial additional work is required to provide adequate mitigations in this space.
