Minimal Knowledge PT-AE Attacks on Black-Box Speaker Recognition Modelsby@botbeat

Minimal Knowledge PT-AE Attacks on Black-Box Speaker Recognition Models

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Investigating minimal knowledge PT-AE attacks on black-box speaker recognition, this study showcases unprecedented ASR success and superior perceptual quality, revolutionizing the attack landscape.
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(1) Rui Duan University of South Florida Tampa, USA (email: [email protected]);

(2) Zhe Qu Central South University Changsha, China (email: [email protected]);

(3) Leah Ding American University Washington, DC, USA (email: [email protected]);

(4) Yao Liu University of South Florida Tampa, USA (email: [email protected]);

(5) Yao Liu University of South Florida Tampa, USA (email: [email protected]).

Abstract and Intro

Background and Motivation

Parrot Training: Feasibility and Evaluation

PT-AE Generation: A Joint Transferability and Perception Perspective

Optimized Black-Box PT-AE Attacks

Experimental Evaluations

Related Work

Conclusion and References



In this work, we investigated using the minimum knowledge of a target speaker’s speech to attack a black-box target speaker recognition model. We extensively evaluated the feasibility of using state-of-the-art VC methods to generate parrot speech samples to build a PT-surrogate model and the generation methods of PT-AEs. It is shown that PT-AEs can effectively transfer to a black-box target model and the proposed PT-AE attack has achieved higher ASRs and better perceptual quality than existing methods against both digital-line speaker recognition models and commercial smart devices in over-the-air scenarios.


[1] Alexa Voice ID. html?nodeId=GYCXKY2AB2QWZT2X/, 2022. Accessed: 2022-12- 13.

[2] Amazon Alexa., 2022. Accessed: 2022-01-07.

[3] Apple Siri., 2022. Accessed: 2022-12-13.

[4] Fidelity-MyVoice. overview/, 2022. Accessed: 2022-12-13.

[5] Kaldi., 2022. Accessed: 2022-12- 13.

[6] Tencent VPR., 2022. Accessed: 2022-12-13.

[7] AGAIN-VC., 2023. Accessed: 2023-01-07.

[8] Amazon Activities. alexa-check-my-balance-amazon-echo-can-now-bank-for-you//, 2023. Accessed: 2023-04-18.

[9] AutoVC., 2023. Accessed: 2023-01-07.

[10] FreeVC., 2023. Accessed: 2023- 01-07.

[11] Google Home., 2023. Accessed: 2023-5-05.

[12] Microsoft Azure. cognitive-services/speech-to-text//, 2023. Accessed: 2023-02-07.

[13] PPG-VC., 2023. Accessed: 2023-01-07.

[14] Semitone., 2023. Accessed: 2023-04-20.

[15] VQMIVC., 2023. Accessed: 2023-01-07.

[16] Hadi Abdullah, Washington Garcia, Christian Peeters, Patrick Traynor, Kevin RB Butler, and Joseph Wilson. Practical hidden voice attacks against speech and speaker recognition systems. In Proc. of NDSS, 2019.

[17] Hadi Abdullah, Muhammad Sajidur Rahman, Washington Garcia, Logan Blue, Kevin Warren, Anurag Swarnim Yadav, Tom Shrimpton, and Patrick Traynor. Hear” no evil”, see” kenansville”: Efficient and transferable black-box attacks on speech recognition and voice identification systems. In Proc. of IEEE S&P, 2021.

[18] Alankrita Aggarwal, Mamta Mittal, and Gopi Battineni. Generative adversarial network: An overview of theory and applications. International Journal of Information Management Data Insights, 1(1):100004, 2021.

[19] Supraja Anand, Lisa M Kopf, Rahul Shrivastav, and David A Eddins. Objective indices of perceived vocal strain. Journal of Voice, 33(6):838–845, 2019.

[20] Yogesh Balaji, Tom Goldstein, and Judy Hoffman. Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets. arXiv preprint arXiv:1910.08051, 2019.

[21] Mikołaj Binkowski, Jeff Donahue, Sander Dieleman, Aidan Clark, ´ Erich Elsen, Norman Casagrande, Luis C Cobo, and Karen Simonyan. High fidelity speech synthesis with adversarial networks. arXiv preprint arXiv:1909.11646, 2019.

[22] Shelley B Brundage and N. Ratner. Measurement of stuttering frequency in children’s speech. Journal of Fluency Disorders, 14:351– 358, 1989.

[23] Kate Bunton, Raymond D Kent, Joseph R Duffy, John C Rosenbek, and Jane F Kent. Listener agreement for auditory-perceptual ratings of dysarthria. 2007.

[24] Lei Cai, Hongyang Gao, and Shuiwang Ji. Multi-stage variational auto-encoders for coarse-to-fine image generation. In Proceedings of the 2019 SIAM International Conference on Data Mining, pages 630– 638. SIAM, 2019.

[25] Qi-Zhi Cai, Min Du, Chang Liu, and Dawn Song. Curriculum adversarial training. In Proc. of IJCAI, 2018.

[26] Nicholas Carlini, Pratyush Mishra, Tavish Vaidya, Yuankai Zhang, Micah Sherr, Clay Shields, David Wagner, and Wenchao Zhou. Hidden voice commands. In Proc. of USENIX Security, 2016.

[27] Nicholas Carlini and David Wagner. Towards evaluating the robustness of neural networks. In Proc. of IEEE S&P, 2017.

[28] Nicholas Carlini and David Wagner. Audio adversarial examples: Targeted attacks on speech-to-text. In Proc. of SPW, 2018.

[29] Guangke Chen, Sen Chen, Lingling Fan, Xiaoning Du, Zhe Zhao, Fu Song, and Yang Liu. Who is real bob? adversarial attacks on speaker recognition systems. In Proc. of IEEE S&P, 2021.

[30] Guangke Chen, Yedi Zhang, Zhe Zhao, and Fu Song. Qfa2sr: Queryfree adversarial transfer attacks to speaker recognition systems. arXiv preprint arXiv:2305.14097, 2023.

[31] Yen-Hao Chen, Da-Yi Wu, Tsung-Han Wu, and Hung-yi Lee. Againvc: A one-shot voice conversion using activation guidance and adaptive instance normalization. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5954–5958. IEEE, 2021.

[32] Yuxuan Chen, Xuejing Yuan, Jiangshan Zhang, Yue Zhao, Shengzhi Zhang, Kai Chen, and XiaoFeng Wang. Devil’s whisper: A general approach for physical adversarial attacks against commercial blackbox speech recognition devices. In Proc. of USENIX Security, 2020.

[33] Zhehuai Chen, Andrew Rosenberg, Yu Zhang, Gary Wang, Bhuvana Ramabhadran, and Pedro J Moreno. Improving speech recognition using gan-based speech synthesis and contrastive unspoken text selection. In Interspeech, pages 556–560, 2020.

[34] Ju-chieh Chou, Cheng-chieh Yeh, and Hung-yi Lee. One-shot voice conversion by separating speaker and content representations with instance normalization. arXiv preprint arXiv:1904.05742, 2019.

[35] Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A Bharath. Generative adversarial networks: An overview. IEEE signal processing magazine, 35(1):53–65, 2018.

[36] Frederic L Darley, Arnold E Aronson, and Joe R Brown. Differential diagnostic patterns of dysarthria. Journal of speech and hearing research, 12(2):246–269, 1969.

[37] Jesse Davis and Mark Goadrich. The relationship between precisionrecall and roc curves. In Proceedings of the 23rd international conference on Machine learning, pages 233–240, 2006.

[38] Najim Dehak, Patrick J Kenny, Reda Dehak, Pierre Dumouchel, and ´ Pierre Ouellet. Front-end factor analysis for speaker verification. IEEE Transactions on Audio, Speech, and Language Processing, 19(4):788– 798, 2010.

[39] Jiangyi Deng, Yanjiao Chen, and Wenyuan Xu. Fencesitter: Black-box, content-agnostic, and synchronization-free enrollment-phase attacks on speaker recognition systems. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, pages 755– 767, 2022.

[40] Jiangyi Deng, Yanjiao Chen, Yinan Zhong, Qianhao Miao, Xueluan Gong, and Wenyuan Xu. Catch you and i can: Revealing source voiceprint against voice conversion. arXiv preprint arXiv:2302.12434, 2023.

[41] Brecht Desplanques, Jenthe Thienpondt, and Kris Demuynck. Ecapatdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification. arXiv preprint arXiv:2005.07143, 2020.

[42] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 9185–9193, 2018.

[43] Tianyu Du, Shouling Ji, Jinfeng Li, Qinchen Gu, Ting Wang, and Raheem Beyah. Sirenattack: Generating adversarial audio for endto-end acoustic systems. In Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, pages 357– 369, 2020.

[44] Rui Duan, Zhe Qu, Shangqing Zhao, Leah Ding, Yao Liu, and Zhuo Lu. Perception-aware attack: Creating adversarial music via reverse engineering human perception. In Proc. of ACM CCS, pages 905–919, 2022.

[45] Cesar Ferri, Peter Flach, and Jos ´ e Hern ´ andez-Orallo. Learning decision ´ trees using the area under the roc curve. In Icml, volume 2, pages 139– 146, 2002.

[46] Lianli Gao, Qilong Zhang, Jingkuan Song, Xianglong Liu, and Heng Tao Shen. Patch-wise attack for fooling deep neural network. In European Conference on Computer Vision, pages 307–322. Springer, 2020.

[47] Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter. Audio set: An ontology and human-labeled dataset for audio events. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 776–780. IEEE, 2017.

[48] Laurent Girin, Simon Leglaive, Xiaoyu Bie, Julien Diard, Thomas Hueber, and Xavier Alameda-Pineda. Dynamical variational autoencoders: A comprehensive review. arXiv preprint arXiv:2008.12595, 2020.

[49] Tom Goldstein, Christoph Studer, and Richard Baraniuk. A field guide to forward-backward splitting with a fasta implementation. arXiv preprint arXiv:1411.3406, 2014.

[50] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139–144, 2020.

[51] Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014.

[52] Hanqing Guo, Yuanda Wang, Nikolay Ivanov, Li Xiao, and Qiben Yan. Specpatch: Human-in-the-loop adversarial audio spectrogram patch attack on speech recognition. 2022.

[53] William Harvey, Saeid Naderiparizi, and Frank Wood. Conditional image generation by conditioning variational auto-encoders. arXiv preprint arXiv:2102.12037, 2021.

[54] Jan Hauke and Tomasz Kossowski. Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones geographicae, 30(2):87–93, 2011.

[55] Wei-Ning Hsu, Yu Zhang, Ron J Weiss, Heiga Zen, Yonghui Wu, Yuxuan Wang, Yuan Cao, Ye Jia, Zhifeng Chen, Jonathan Shen, et al. Hierarchical generative modeling for controllable speech synthesis. arXiv preprint arXiv:1810.07217, 2018.

[56] Wenbin Huang, Wenjuan Tang, Hongbo Jiang, Jun Luo, and Yaoxue Zhang. Stop deceiving! an effective defense scheme against voice impersonation attacks on smart devices. IEEE Internet of Things Journal, 9(7):5304–5314, 2021.

[57] Sergey Ioffe. Probabilistic linear discriminant analysis. In European Conference on Computer Vision, pages 531–542. Springer, 2006.

[58] Arindam Jati, Chin-Cheng Hsu, Monisankha Pal, Raghuveer Peri, Wael AbdAlmageed, and Shrikanth Narayanan. Adversarial attack and defense strategies for deep speaker recognition systems. Computer Speech & Language, 68:101199, 2021.

[59] Takuhiro Kaneko, Hirokazu Kameoka, Nobukatsu Hojo, Yusuke Ijima, Kaoru Hiramatsu, and Kunio Kashino. Generative adversarial networkbased postfilter for statistical parametric speech synthesis. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 4910–4914. IEEE, 2017.

[60] Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka, and Nobukatsu Hojo. Cyclegan-vc2: Improved cyclegan-based non-parallel voice conversion. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6820–6824. IEEE, 2019.

[61] Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka, and Nobukatsu Hojo. Stargan-vc2: Rethinking conditional methods for stargan-based voice conversion. arXiv preprint arXiv:1907.12279, 2019.

[62] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.

[63] Tom Ko, Vijayaditya Peddinti, Daniel Povey, and Sanjeev Khudanpur. Audio augmentation for speech recognition. In Sixteenth annual conference of the international speech communication association, 2015.

[64] Tom Ko, Vijayaditya Peddinti, Daniel Povey, Michael L Seltzer, and Sanjeev Khudanpur. A study on data augmentation of reverberant speech for robust speech recognition. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5220–5224. IEEE, 2017.

[65] Jungil Kong, Jaehyeon Kim, and Jaekyoung Bae. Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. Advances in Neural Information Processing Systems, 33:17022–17033, 2020.

[66] Alexandru Korotcov, Valery Tkachenko, Daniel P Russo, and Sean Ekins. Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets. Molecular pharmaceutics, 14(12):4462–4475, 2017.

[67] Kong Aik Lee, Qiongqiong Wang, and Takafumi Koshinaka. The coral+ algorithm for unsupervised domain adaptation of plda. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5821–5825. IEEE, 2019.

[68] Chao Li, Xiaokong Ma, Bing Jiang, Xiangang Li, Xuewei Zhang, Xiao Liu, Ying Cao, Ajay Kannan, and Zhenyao Zhu. Deep speaker: an end-to-end neural speaker embedding system. arXiv preprint arXiv:1705.02304, 2017.

[69] Jason Li, Ravi Gadde, Boris Ginsburg, and Vitaly Lavrukhin. Training neural speech recognition systems with synthetic speech augmentation. arXiv preprint arXiv:1811.00707, 2018.

[70] Jingyi Li, Weiping Tu, and Li Xiao. Freevc: Towards high-quality text-free one-shot voice conversion.

[71] Yuanchun Li, Ziqi Zhang, Bingyan Liu, Ziyue Yang, and Yunxin Liu. Modeldiff: testing-based dnn similarity comparison for model reuse detection. In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, pages 139–151, 2021.

[72] Zhuohang Li, Yi Wu, Jian Liu, Yingying Chen, and Bo Yuan. Advpulse: Universal, synchronization-free, and targeted audio adversarial attacks via subsecond perturbations. In Proc. of ACM CCS, pages 1121–1134, 2020.

[73] Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and John E Hopcroft. Nesterov accelerated gradient and scale invariance for adversarial attacks. arXiv preprint arXiv:1908.06281, 2019.

[74] Han Liu, Zhiyuan Yu, Mingming Zha, XiaoFeng Wang, William Yeoh, Yevgeniy Vorobeychik, and Ning Zhang. When evil calls: Targeted adversarial voice over ip network. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, pages 2009–2023, 2022.

[75] Songxiang Liu, Yuewen Cao, Disong Wang, Xixin Wu, Xunying Liu, and Helen Meng. Any-to-many voice conversion with location-relative sequence-to-sequence modeling. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29:1717–1728, 2021.

[76] Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. Delving into transferable adversarial examples and black-box attacks. arXiv preprint arXiv:1611.02770, 2016.

[77] Hui Lu, Zhiyong Wu, Dongyang Dai, Runnan Li, Shiyin Kang, Jia Jia, and Helen Meng. One-shot voice conversion with global speaker embeddings. In Interspeech, pages 669–673, 2019.

[78] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In Proc. of ICML Work Shop, 2017.

[79] Yuhao Mao, Chong Fu, Saizhuo Wang, Shouling Ji, Xuhong Zhang, Zhenguang Liu, Jun Zhou, Alex X Liu, Raheem Beyah, and Ting Wang. Transfer attacks revisited: A large-scale empirical study in real computer vision settings. arXiv preprint arXiv:2204.04063, 2022.

[80] Agnieszka Mikołajczyk and Michał Grochowski. Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW), pages 117– 122. IEEE, 2018.

[81] M. Mines, Barbara F. Hanson, and J. Shoup. Frequency of occurrence of phonemes in conversational english. Language and Speech, 21:221 – 241, 1978.

[82] Gabriel Mittag, Babak Naderi, Assmaa Chehadi, and Sebastian Moller. Nisqa: A deep cnn-self-attention model for multidimensional ¨ speech quality prediction with crowdsourced datasets. arXiv preprint arXiv:2104.09494, 2021.

[83] Arsha Nagrani, Joon Son Chung, and Andrew Zisserman. Voxceleb: a large-scale speaker identification dataset. arXiv preprint arXiv:1706.08612, 2017.

[84] Preeti Nagrath, Rachna Jain, Agam Madan, Rohan Arora, Piyush Kataria, and Jude Hemanth. Ssdmnv2: A real time dnn-based face mask detection system using single shot multibox detector and mobilenetv2. Sustainable cities and society, 66:102692, 2021.

[85] Mahesh Kumar Nandwana, Luciana Ferrer, Mitchell McLaren, Diego Castan, and Aaron Lawson. Analysis of critical metadata factors for the calibration of speaker recognition systems. In INTERSPEECH, pages 4325–4329, 2019.

[86] Phani Sankar Nidadavolu, Vicente Iglesias, Jesus Villalba, and Najim ´ Dehak. Investigation on neural bandwidth extension of telephone speech for improved speaker recognition. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6111–6115. IEEE, 2019.

[87] Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. Librispeech: an asr corpus based on public domain audio books. In 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 5206–5210. IEEE, 2015.

[88] Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow. Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277, 2016.

[89] Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. Practical black-box attacks against machine learning. In Proceedings of the 2017 ACM on Asia conference on computer and communications security, pages 506–519, 2017.

[90] Daniel S Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D Cubuk, and Quoc V Le. Specaugment: A simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779, 2019.

[91] Sona Patel, Rahul Shrivastav, and David A Eddins. Perceptual distances of breathy voice quality: A comparison of psychophysical methods. Journal of Voice, 24(2):168–177, 2010.

[92] Luis Perez and Jason Wang. The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621, 2017.

[93] Daniel Povey, Arnab Ghoshal, Gilles Boulianne, Lukas Burget, Ondrej Glembek, Nagendra Goel, Mirko Hannemann, Petr Motlicek, Yanmin Qian, Petr Schwarz, et al. The kaldi speech recognition toolkit. In IEEE 2011 workshop on automatic speech recognition and understanding, number CONF. IEEE Signal Processing Society, 2011.

[94] Kaizhi Qian, Yang Zhang, Shiyu Chang, Xuesong Yang, and Mark Hasegawa-Johnson. Autovc: Zero-shot voice style transfer with only autoencoder loss. In International Conference on Machine Learning, pages 5210–5219. PMLR, 2019.

[95] Yao Qin, Nicholas Carlini, Garrison Cottrell, Ian Goodfellow, and Colin Raffel. Imperceptible, robust, and targeted adversarial examples for automatic speech recognition. In Proc. of ICML, pages 5231–5240. PMLR, 2019.

[96] Douglas A Reynolds, Thomas F Quatieri, and Robert B Dunn. Speaker verification using adapted gaussian mixture models. Digital signal processing, 10(1-3):19–41, 2000.

[97] Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein. Adversarial training for free! In Proc. of NIPS, 2019.

[98] Connor Shorten and Taghi M Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of big data, 6(1):1–48, 2019.

[99] David Snyder, Daniel Garcia-Romero, Daniel Povey, and Sanjeev Khudanpur. Deep neural network embeddings for text-independent speaker verification. In Interspeech, volume 2017, pages 999–1003, 2017.

[100] David Snyder, Daniel Garcia-Romero, Gregory Sell, Daniel Povey, and Sanjeev Khudanpur. X-vectors: Robust dnn embeddings for speaker recognition. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 5329–5333. IEEE, 2018.

[101] Rohan Taori, Amog Kamsetty, Brenton Chu, and Nikita Vemuri. Targeted adversarial examples for black box audio systems. In 2019 IEEE Security and Privacy Workshops (SPW), pages 15–20. IEEE, 2019.

[102] Florian Tramer, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, ` Dan Boneh, and Patrick McDaniel. Ensemble adversarial training: Attacks and defenses. In Proc. of ICLR, 2018.

[103] Christophe Veaux, Junichi Yamagishi, Kirsten MacDonald, et al. Superseded-cstr vctk corpus: English multi-speaker corpus for cstr voice cloning toolkit. 2016.

[104] Disong Wang, Liqun Deng, Yu Ting Yeung, Xiao Chen, Xunying Liu, and Helen Meng. Vqmivc: Vector quantization and mutual information-based unsupervised speech representation disentanglement for one-shot voice conversion. arXiv preprint arXiv:2106.10132, 2021.

[105] Qian Wang, Baolin Zheng, Qi Li, Chao Shen, and Zhongjie Ba. Towards query-efficient adversarial attacks against automatic speech recognition systems. IEEE Transactions on Information Forensics and Security, 16:896–908, 2020.

[106] Xiaosen Wang and Kun He. Enhancing the transferability of adversarial attacks through variance tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1924– 1933, 2021.

[107] Emily Wenger, Max Bronckers, Christian Cianfarani, Jenna Cryan, Angela Sha, Haitao Zheng, and Ben Y Zhao. ” hello, it’s me”: Deep learning-based speech synthesis attacks in the real world. In Proc. of ACM CCS, pages 235–251, 2021.

[108] M. Wester and R. Karhila. Speaker similarity evaluation of foreignaccented speech synthesis using HMM-based speaker adaptation. In Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5372–5375, 2011.

[109] Eric Wong, Leslie Rice, and J Zico Kolter. Fast is better than free: Revisiting adversarial training. arXiv preprint arXiv:2001.03994, 2020.

[110] Da-Yi Wu and Hung-yi Lee. One-shot voice conversion by vector quantization. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 7734– 7738. IEEE, 2020.

[111] Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille. Improving transferability of adversarial examples with input diversity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2730– 2739, 2019.

[112] Zhuolin Yang, Bo Li, Pin-Yu Chen, and Dawn Song. Characterizing audio adversarial examples using temporal dependency. arXiv preprint arXiv:1809.10875, 2018.

[113] Zhiyuan Yu, Yuanhaur Chang, Ning Zhang, and Chaowei Xiao. Smack: Semantically meaningful adversarial audio attack. 2023.

[114] Xuejing Yuan, Yuxuan Chen, Yue Zhao, Yunhui Long, Xiaokang Liu, Kai Chen, Shengzhi Zhang, Heqing Huang, XiaoFeng Wang, and Carl A Gunter. Commandersong: A systematic approach for practical adversarial voice recognition. In Proc. of USENIX Security, 2018.

[115] Eiji Yumoto, Wilbur J Gould, and Thomas Baer. Harmonics-to-noise ratio as an index of the degree of hoarseness. The journal of the Acoustical Society of America, 71(6):1544–1550, 1982.

[116] Guoming Zhang, Chen Yan, Xiaoyu Ji, Tianchen Zhang, Taimin Zhang, and Wenyuan Xu. Dolphinattack: Inaudible voice commands. In Proc. of ACM CCS, pages 103–117, 2017.

[117] Ya-Jie Zhang, Shifeng Pan, Lei He, and Zhen-Hua Ling. Learning latent representations for style control and transfer in end-to-end speech synthesis. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6945–6949. IEEE, 2019.

[118] Baolin Zheng, Peipei Jiang, Qian Wang, Qi Li, Chao Shen, Cong Wang, Yunjie Ge, Qingyang Teng, and Shenyi Zhang. Black-box adversarial attacks on commercial speech platforms with minimal information. In Proc. of ACM CCS, 2021.

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