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Learning to Fool the Speaker Recognition

文献类型：期刊论文

作者Li, Jiguo1,2; Zhang, Xinfeng3; Xu, Jizheng4; Ma, Siwei5; Gao, Wen5
刊名ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
出版日期2021-10-01
卷号17期号:3页码:21
关键词Audio forensics adversarial attack deep neural network
ISSN号1551-6857
DOI10.1145/3468673
英文摘要Due to the widespread deployment of fingerprint/face/speaker recognition systems, the risk in these systems, especially the adversarial attack, has drawn increasing attention in recent years. Previous researches mainly studied the adversarial attack to the vision-based systems, such as fingerprint and face recognition. While the attack for speech-based systems has not been well studied yet, although it has been widely used in our daily life. In this article, we attempt to fool the state-of-the-art speaker recognition model and present speaker recognition attacker, a lightweight multi-layer convolutional neural network to fool the well-trained state-of-the-art speaker recognition model by adding imperceptible perturbations onto the raw speech waveform. We find that the speaker recognition system is vulnerable to the adversarial attack, and achieve a high success rate on both the non-targeted attack and targeted attack. Besides, we present an effective method by leveraging a pretrained phoneme recognition model to optimize the speaker recognition attacker to obtain a tradeoff between the attack success rate and the perceptual quality. Experimental results on the TIMIT and LibriSpeech datasets demonstrate the effectiveness and efficiency of our proposed model. And the experiments for frequency analysis indicate that high-frequency attack is more effective than low-frequency attack, which is different from the conclusion drawn in previous image-based works. Additionally, the ablation study gives more insights into our model.
资助项目National Science Foundation of China[62025101620] ; National Science Foundation of China[61961130392] ; PKU-Baidu Fund[2019BD003] ; High-performance Computing Platform of Peking University
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000738280600011
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/18368]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Ma, Siwei
作者单位1.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
4.Bytedance Inc, 48A Zhichun Rd, Beijing 100191, Peoples R China
5.Peking Univ, 5 Yiheyuan Rd, Beijing 100871, Peoples R China
推荐引用方式
GB/T 7714		 Li, Jiguo,Zhang, Xinfeng,Xu, Jizheng,et al. Learning to Fool the Speaker Recognition[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2021,17(3):21.
APA Li, Jiguo,Zhang, Xinfeng,Xu, Jizheng,Ma, Siwei,&Gao, Wen.(2021).Learning to Fool the Speaker Recognition.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,17(3),21.
MLA Li, Jiguo,et al."Learning to Fool the Speaker Recognition".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 17.3(2021):21.

入库方式： OAI收割

来源：计算技术研究所

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