Towards Vulnerability Analysis of Voice-Driven Interfaces and Countermeasures for Replay Attacks
Towards Vulnerability Analysis of Voice-Driven Interfaces and Countermeasures for Replay Attacks
复制标题
DOI：
10.1109/mipr.2019.00106
复制
发表时间：
2019-03
期刊：
影响因子：
--
通讯作者：
K. Malik;Hafiz Malik;Roland Baumann
中科院分区：
文献类型：
--
作者：
K. Malik;Hafiz Malik;Roland Baumann
虚假音频检测有望成为谷歌主页、亚马逊回声和为这些平台开发的聊天机器人等智能扬声器领域的重要研究领域。文章指出了语音驱动接口的重放攻击漏洞，并提出了检测这些平台上重放攻击的对策。提出了一种新的重放攻击失真建模框架，并利用一种非学习的方法对智能扬声器的重放攻击进行检测。回复攻击失真被建模为重放攻击音频中的高阶非线性。高阶谱分析(HOSA)用于捕获重放音频中的特征失真。在原始语音和相应的重放录音上对所提出的对抗方案的有效性进行了评估。使用即插即用会议功能，通过Amazon Alexa成功地将重播攻击录音注入Google Home设备。
Fake audio detection is expected to become an important research area in the field of smart speakers such as Google Home, Amazon Echo and chatbots developed for these platforms. This paper presents replay attack vulnerability of voice-driven interfaces and proposes a countermeasure to detect replay attack on these platforms. This paper presents a novel framework to model replay attack distortion, and then use a non-learning-based method for replay attack detection on smart speakers. The reply attack distortion is modeled as a higher-order nonlinearity in the replay attack audio. Higher-order spectral analysis (HOSA) is used to capture characteristics distortions in the replay audio. Effectiveness of the proposed countermeasure scheme is evaluated on original speech as well as corresponding replayed recordings. The replay attack recordings are successfully injected into the Google Home device via Amazon Alexa using the drop-in conferencing feature.