{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T12:36:22Z","timestamp":1783427782951,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":67,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF2010141"],"award-info":[{"award-number":["W911NF2010141"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-1916926, CNS-2038995, CNS-2154930, CNS-2238635"],"award-info":[{"award-number":["CNS-1916926, CNS-2038995, CNS-2154930, CNS-2238635"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,15]]},"DOI":"10.1145\/3576915.3623209","type":"proceedings-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T12:35:13Z","timestamp":1700570113000},"page":"460-474","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["AntiFake: Using Adversarial Audio to Prevent Unauthorized Speech Synthesis"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6196-7598","authenticated-orcid":false,"given":"Zhiyuan","family":"Yu","sequence":"first","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8391-1829","authenticated-orcid":false,"given":"Shixuan","family":"Zhai","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0670-2161","authenticated-orcid":false,"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"26th Annual Network and Distributed System Security Symposium, NDSS 2019","author":"Hadi","year":"2019","unstructured":"Hadi Abdullah et al. 2019. Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems. In 26th Annual Network and Distributed System Security Symposium, NDSS 2019, San Diego, California, USA, February 24-27, 2019."},{"key":"e_1_3_2_1_2_1","volume-title":"2021 IEEE Symposium on Security and Privacy (SP). IEEE, 712--729","author":"Hadi","unstructured":"Hadi Abdullah et al. 2021. Hear \"No Eil\", See \"Kenansville\"*: Efficient and transferable black-box attacks on speech recognition and voice identification systems. In 2021 IEEE Symposium on Security and Privacy (SP). IEEE, 712--729."},{"key":"e_1_3_2_1_3_1","volume-title":"29th USENIX Security Symposium (USENIX Security 20)","author":"Muhammad Ejaz","unstructured":"Muhammad Ejaz Ahmed et al. 2020. Void: A fast and light voice liveness detection system. In 29th USENIX Security Symposium (USENIX Security 20). 2685--2702."},{"key":"e_1_3_2_1_4_1","unstructured":"James Betker. 2022. TorToiSe TTS. https:\/\/github.com\/neonbjb\/tortoise-tts."},{"key":"e_1_3_2_1_5_1","volume-title":"Detecting Audio DeepFakes Through Vocal Tract Reconstruction. In 31st USENIX Security Symposium, USENIX Security","author":"Logan","year":"2022","unstructured":"Logan Blue et al. 2022. Who Are You (I Really Wanna Know)? Detecting Audio DeepFakes Through Vocal Tract Reconstruction. In 31st USENIX Security Symposium, USENIX Security 2022. USENIX Association, 2691--2708."},{"key":"e_1_3_2_1_6_1","volume-title":"The Journal of the Acoustical Society of America","volume":"109","author":"Douglas S.","year":"2001","unstructured":"Douglas S. Brungart. 2001. Informational and energetic masking effects in the perception of two simultaneous talkers. The Journal of the Acoustical Society of America, Vol. 109, 3 (03 2001), 1101--1109."},{"key":"e_1_3_2_1_7_1","volume-title":"25th USENIX Security Symposium (USENIX Security 16)","author":"Nicholas","unstructured":"Nicholas Carlini et al. 2016. Hidden voice commands. In 25th USENIX Security Symposium (USENIX Security 16). 513--530."},{"key":"e_1_3_2_1_8_1","volume-title":"2018 IEEE Security and Privacy Workshops (SPW). IEEE, 1--7.","author":"Nicholas","unstructured":"Nicholas Carlini et al. 2018. Audio adversarial examples: Targeted attacks on speech-to-text. In 2018 IEEE Security and Privacy Workshops (SPW). IEEE, 1--7."},{"key":"e_1_3_2_1_9_1","volume-title":"International Conference on Machine Learning. PMLR, 2709--2720","author":"Edresson","unstructured":"Edresson Casanova et al. 2022. Yourtts: Towards zero-shot multi-speaker tts and zero-shot voice conversion for everyone. In International Conference on Machine Learning. PMLR, 2709--2720."},{"key":"e_1_3_2_1_10_1","volume-title":"2021 IEEE Symposium on Security and Privacy (SP). IEEE.","author":"Guangke","unstructured":"Guangke Chen et al. 2021. Who is real bob? adversarial attacks on speaker recognition systems. In 2021 IEEE Symposium on Security and Privacy (SP). IEEE."},{"key":"e_1_3_2_1_11_1","volume-title":"27th Annual Network and Distributed System Security Symposium, NDSS 2020","author":"Tao","year":"2020","unstructured":"Tao Chen et al. 2020. Metamorph: Injecting Inaudible Commands into Over-the-air Voice Controlled Systems. In 27th Annual Network and Distributed System Security Symposium, NDSS 2020, San Diego, California, USA, February 23-26, 2020."},{"key":"e_1_3_2_1_12_1","volume-title":"9th International Conference on Learning Representations, ICLR 2021","author":"Valeriia","year":"2021","unstructured":"Valeriia Cherepanova et al. 2021. LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition. In 9th International Conference on Learning Representations, ICLR 2021, Austria, May 3-7, 2021."},{"key":"e_1_3_2_1_13_1","volume-title":"20th Annual Conference of the International Speech Communication Association. ISCA, 664--668","author":"Ju-Chieh","unstructured":"Ju-Chieh Chou et al. 2019. One-Shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization. In 20th Annual Conference of the International Speech Communication Association. ISCA, 664--668."},{"key":"e_1_3_2_1_14_1","unstructured":"Graham Cluley. 2022. Deepfaking crooks seek remote-working jobs to gain access to sensitive data. https:\/\/grahamcluley.com\/deepfaking-crooks-seek-remote-working-jobs-to-gain-access-to-sensitive-data\/."},{"key":"e_1_3_2_1_15_1","unstructured":"Joseph Cox. 2023. How I Broke Into a Bank Account With an AI-Generated Voice. https:\/\/www.vice.com\/en\/article\/dy7axa\/how-i-broke-into-a-bank-account-with-an-ai-generated-voice."},{"key":"e_1_3_2_1_16_1","first-page":"4","article-title":"2010. Front-end factor analysis for speaker verification","volume":"19","author":"Najim Dehak","year":"2010","unstructured":"Najim Dehak et al. 2010. Front-end factor analysis for speaker verification. IEEE Transactions on Audio, Speech, and Language Processing, Vol. 19, 4 (2010), 788--798.","journal-title":"IEEE Transactions on Audio, Speech, and Language Processing"},{"key":"e_1_3_2_1_17_1","volume-title":"9th International Conference on Learning Representations, ICLR 2021","author":"Alexey","year":"2021","unstructured":"Alexey Dosovitskiy et al. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021."},{"key":"e_1_3_2_1_18_1","unstructured":"ElevenLabs. 2023. Prime Voice AI. https:\/\/beta.elevenlabs.io\/."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"John S Garofolo et al. 1993. DARPA TIMIT acoustic-phonetic continous speech corpus CD-ROM. NIST speech disc 1--1.1. NASA STI\/Recon technical report (1993).","DOI":"10.6028\/NIST.IR.4930"},{"key":"e_1_3_2_1_20_1","volume-title":"Explaining and Harnessing Adversarial Examples. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings.","author":"Ian J.","unstructured":"Ian J. Goodfellow et al. 2015. Explaining and Harnessing Adversarial Examples. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings."},{"key":"e_1_3_2_1_21_1","first-page":"5","article-title":"2022. Gender identification in a two-level hierarchical speech emotion recognition system for an Italian Social Robot","volume":"22","author":"Antonio Guerrieri","year":"2022","unstructured":"Antonio Guerrieri et al. 2022. Gender identification in a two-level hierarchical speech emotion recognition system for an Italian Social Robot. Sensors, Vol. 22, 5 (2022), 1714.","journal-title":"Sensors"},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security. 1353--1366","author":"Hanqing","unstructured":"Hanqing Guo et al. 2022. Specpatch: Human-in-the-loop adversarial audio spectrogram patch attack on speech recognition. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security. 1353--1366."},{"key":"e_1_3_2_1_23_1","unstructured":"Hee Soo Heo et al. 2020. Clova baseline system for the voxceleb speaker recognition challenge 2020. arXiv preprint arXiv:2009.14153 (2020)."},{"key":"e_1_3_2_1_24_1","first-page":"2","article-title":"2009. Ossicular resonance modes of the human middle ear for bone and air conduction","volume":"125","author":"Kenji Homma","year":"2009","unstructured":"Kenji Homma et al. 2009. Ossicular resonance modes of the human middle ear for bone and air conduction. The Journal of the Acoustical Society of America, Vol. 125, 2 (2009), 968--979.","journal-title":"The Journal of the Acoustical Society of America"},{"key":"e_1_3_2_1_25_1","unstructured":"HSBC. 2016. How do I sign up for Voice ID? https:\/\/www.hsbc.co.uk\/ways-to-bank\/phone-banking\/."},{"key":"e_1_3_2_1_26_1","volume-title":"2021 IEEE Spoken Language Technology Workshop (SLT). IEEE.","author":"Chien","unstructured":"Chien-yu Huang et al. 2021. Defending your voice: Adversarial attack on voice conversion. In 2021 IEEE Spoken Language Technology Workshop (SLT). IEEE."},{"key":"e_1_3_2_1_27_1","volume-title":"30th USENIX Security Symposium, USENIX Security 2021","author":"Shehzeen","year":"2021","unstructured":"Shehzeen Hussain et al. 2021. {WaveGuard}: Understanding and Mitigating Audio Adversarial Examples. In 30th USENIX Security Symposium, USENIX Security 2021, August 11-13, 2021. USENIX Association, 2273--2290."},{"key":"e_1_3_2_1_28_1","volume-title":"22nd Annual Conference of the International Speech Communication Association. ISCA, 2207--2211","author":"Won","unstructured":"Won Jang et al. 2021. UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation. In Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association. ISCA, 2207--2211."},{"key":"e_1_3_2_1_29_1","volume-title":"https:\/\/github.com\/CorentinJ\/Real-Time-Voice-Cloning","author":"Corentin Jemine","year":"2019","unstructured":"Corentin Jemine. 2019. Real-time-voice-cloning. https:\/\/github.com\/CorentinJ\/Real-Time-Voice-Cloning. University of Li\u00e9ge, Li\u00e9ge, Belgium (2019)."},{"key":"e_1_3_2_1_30_1","volume-title":"Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems","author":"Ye","year":"2018","unstructured":"Ye Jia et al. 2018. Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018. 4485--4495."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Tomi Kinnunen et al. 2010. An overview of text-independent speaker recognition: From features to supervectors. Speech communication Vol. 52 1 (2010).","DOI":"10.1016\/j.specom.2009.08.009"},{"key":"e_1_3_2_1_32_1","first-page":"17022","article-title":"2020. Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis","volume":"33","author":"Jungil Kong","year":"2020","unstructured":"Jungil Kong et al. 2020. Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. Advances in Neural Information Processing Systems, Vol. 33 (2020), 17022--17033.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_33_1","volume-title":"2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE","author":"Felix","unstructured":"Felix Kreuk et al. 2018. Fooling end-to-end speaker verification with adversarial examples. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 1962--1966."},{"key":"e_1_3_2_1_34_1","volume-title":"Proceedings of the 21st international workshop on mobile computing systems and applications.","author":"Zhuohang","unstructured":"Zhuohang Li et al. 2020. Practical adversarial attacks against speaker recognition systems. In Proceedings of the 21st international workshop on mobile computing systems and applications."},{"key":"e_1_3_2_1_35_1","volume-title":"22nd Annual Conference of the International Speech Communication Association. ISCA, 2127--2131","author":"Gabriel","unstructured":"Gabriel Mittag et al. 2021. NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets. In Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association. ISCA, 2127--2131."},{"key":"e_1_3_2_1_36_1","first-page":"19","article-title":"2019","volume":"9","author":"Yishuang Ning","year":"2019","unstructured":"Yishuang Ning et al. 2019. A Review of Deep Learning Based Speech Synthesis. Applied Sciences, Vol. 9, 19 (2019).","journal-title":"A Review of Deep Learning Based Speech Synthesis. Applied Sciences"},{"key":"e_1_3_2_1_37_1","volume-title":"2015 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE.","author":"Vassil","unstructured":"Vassil Panayotov et al. 2015. Librispeech: an asr corpus based on public domain audio books. In 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE."},{"key":"e_1_3_2_1_38_1","volume-title":"IEEE 2011 workshop on automatic speech recognition and understanding. IEEE Signal Processing Society.","author":"Daniel","unstructured":"Daniel Povey et al. 2011. The Kaldi speech recognition toolkit. In IEEE 2011 workshop on automatic speech recognition and understanding. IEEE Signal Processing Society."},{"key":"e_1_3_2_1_39_1","volume-title":"International Conference on Machine Learning. PMLR, 5210--5219","author":"Kaizhi","unstructured":"Kaizhi Qian et al. 2019. Autovc: Zero-shot voice style transfer with only autoencoder loss. In International Conference on Machine Learning. PMLR, 5210--5219."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Douglas A Reynolds et al. 2000. Speaker verification using adapted Gaussian mixture models. Digital signal processing Vol. 10 1--3 (2000) 19--41.","DOI":"10.1006\/dspr.1999.0361"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Jeff Sauro et al. 2016. Quantifying the user experience: Practical statistics for user research. Morgan Kaufmann.","DOI":"10.1016\/B978-0-12-802308-2.00002-3"},{"key":"e_1_3_2_1_42_1","volume-title":"26th Annual Network and Distributed System Security Symposium (NDSS).","author":"Lea","unstructured":"Lea Sch\u00f6 nherr et al. 2019. Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding. In 26th Annual Network and Distributed System Security Symposium (NDSS)."},{"key":"e_1_3_2_1_43_1","volume-title":"29th USENIX Security Symposium, USENIX Security","author":"Shawn","year":"2020","unstructured":"Shawn Shan et al. 2020. Fawkes: Protecting Privacy against Unauthorized Deep Learning Models. In 29th USENIX Security Symposium, USENIX Security 2020."},{"key":"e_1_3_2_1_44_1","volume-title":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 28--36","author":"Jiacheng","unstructured":"Jiacheng Shang et al. 2018. Defending against voice spoofing: A robust software-based liveness detection system. In 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 28--36."},{"key":"e_1_3_2_1_45_1","volume-title":"2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 4779--4783","author":"Jonathan","unstructured":"Jonathan Shen et al. 2018. Natural tts synthesis by conditioning wavenet on mel spectrogram predictions. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 4779--4783."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Sayaka Shiota et al. 2015. Voice liveness detection algorithms based on pop noise caused by human breath for automatic speaker verification. In Sixteenth annual conference of the international speech communication association.","DOI":"10.21437\/Interspeech.2015-92"},{"key":"e_1_3_2_1_47_1","unstructured":"Catherine Stupp. 2019. Fraudsters Used AI to Mimic CEO's Voice in Unusual Cybercrime Case. https:\/\/www.wsj.com\/articles\/fraudsters-use-ai-to-mimic-ceos-voice-in-unusual-cybercrime-case-11567157402."},{"key":"e_1_3_2_1_48_1","first-page":"08","article-title":"2004. Equal-loudness-level contours for pure tones","volume":"116","author":"Y\u00f4iti Suzuki","year":"2004","unstructured":"Y\u00f4iti Suzuki et al. 2004. Equal-loudness-level contours for pure tones. The Journal of the Acoustical Society of America, Vol. 116, 2 (08 2004).","journal-title":"The Journal of the Acoustical Society of America"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Rohan Taori et al. 2019. Targeted adversarial examples for black box audio systems. In 2019 IEEE security and privacy workshops (SPW). IEEE 15--20.","DOI":"10.1109\/SPW.2019.00016"},{"key":"e_1_3_2_1_50_1","volume-title":"International journal on emerging technologies","author":"Vibha Tiwari 0.","year":"2010","unstructured":"Vibha Tiwari. 2010. MFCC and its applications in speaker recognition. International journal on emerging technologies, Vol. 1, 1 (2010), 19--22."},{"key":"e_1_3_2_1_51_1","volume-title":"2018 3rd International Conference for Convergence in Technology (I2CT). IEEE, 1--5.","author":"Satyam P","unstructured":"Satyam P Todkar et al. 2018. Speaker recognition techniques: A review. In 2018 3rd International Conference for Convergence in Technology (I2CT). IEEE, 1--5."},{"key":"e_1_3_2_1_52_1","unstructured":"Uberduck. 2023. Text to Voice. https:\/\/app.uberduck.ai\/voice-to-voice. (2023)."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"crossref","unstructured":"Omkarprasad S Vaidya et al. 2006. Analytic hierarchy process: An overview of applications. European Journal of operational research Vol. 169 1 (2006) 1--29.","DOI":"10.1016\/j.ejor.2004.04.028"},{"key":"e_1_3_2_1_54_1","volume-title":"WaveNet: A Generative Model for Raw Audio. In The 9th ISCA Speech Synthesis Workshop","author":"A\u00e4ron","year":"2016","unstructured":"A\u00e4ron van den Oord et al. 2016. WaveNet: A Generative Model for Raw Audio. In The 9th ISCA Speech Synthesis Workshop, September 2016. ISCA, 125."},{"key":"e_1_3_2_1_55_1","volume-title":"Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP'96","volume":"2","author":"Rivarol","unstructured":"Rivarol Vergin et al. 1996. Robust gender-dependent acoustic-phonetic modelling in continuous speech recognition based on a new automatic male\/female classification. In Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP'96, Vol. 2. IEEE."},{"key":"e_1_3_2_1_56_1","unstructured":"James Vincent. 2023. 4chan users embrace AI voice clone tool to generate celebrity hatespeech. https:\/\/www.theverge.com\/2023\/1\/31\/23579289\/ai-voice-clone-deepfake-abuse-4chan-elevenlabs."},{"key":"e_1_3_2_1_57_1","unstructured":"Tarun Wadhwa. 2015. Wells Fargo Wants To Let You Make Million-Dollar Wire Transactions With Your Face And Voice. https:\/\/www.forbes.com\/sites\/tarunwadhwa\/2015\/11\/03\/why-wells-fargo-wants-to-let-you-make-million-dollar-wire-transactions-with-your-face-and-voice\/."},{"key":"e_1_3_2_1_58_1","volume-title":"Generalized End-to-End Loss for Speaker Verification. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2018","author":"Li","year":"2018","unstructured":"Li Wan et al. 2018. Generalized End-to-End Loss for Speaker Verification. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2018, April 2018. IEEE, 4879--4883."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"crossref","unstructured":"Steven H Weinberger et al. 2011. The Speech Accent Archive: towards a typology of English accents. In Corpus-based studies in language use language learning and language documentation. Brill 265--281.","DOI":"10.1163\/9789401206884_014"},{"key":"e_1_3_2_1_60_1","volume-title":"Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security. 235--251","author":"Emily","unstructured":"Emily Wenger et al. 2021. Hello, It's Me: Deep Learning-based Speech Synthesis Attacks in the Real World. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security. 235--251."},{"key":"e_1_3_2_1_61_1","unstructured":"Junichi Yamagishi et al. 2019. CSTR VCTK Corpus: English multi-speaker corpus for CSTR voice cloning toolkit. University of Edinburgh. The Centre for Speech Technology Research (CSTR) (2019)."},{"key":"e_1_3_2_1_62_1","first-page":"3","article-title":"2021. Security and privacy in the emerging cyber-physical world: A survey","volume":"23","author":"Zhiyuan Yu","year":"2021","unstructured":"Zhiyuan Yu et al. 2021. Security and privacy in the emerging cyber-physical world: A survey. IEEE Communications Surveys & Tutorials, Vol. 23, 3 (2021), 1879--1919.","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"e_1_3_2_1_63_1","volume-title":"32nd USENIX Security Symposium (USENIX Security 23)","author":"Zhiyuan","unstructured":"Zhiyuan Yu et al. 2023. {MACK}: Semantically Meaningful Adversarial Audio Attack. In 32nd USENIX Security Symposium (USENIX Security 23). 3799--3816."},{"key":"e_1_3_2_1_64_1","volume-title":"27th USENIX Security Symposium (USENIX Security 18)","author":"Xuejing","unstructured":"Xuejing Yuan et al. 2018. Commandersong: A systematic approach for practical adversarial voice recognition. In 27th USENIX Security Symposium (USENIX Security 18). 49--64."},{"key":"e_1_3_2_1_65_1","unstructured":"Anna Zhadan. 2023. Emma Watson reads Mein Kampf while Biden announces invasion of Russia in latest AI voice clone abuse. https:\/\/cybernews.com\/news\/ai-voice-clone-misuse\/."},{"key":"e_1_3_2_1_66_1","volume-title":"Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 57--71","author":"Linghan","unstructured":"Linghan Zhang et al. 2017. Hearing your voice is not enough: An articulatory gesture based liveness detection for voice authentication. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 57--71."},{"key":"e_1_3_2_1_67_1","volume-title":"2011 IEEE workshop on automatic speech recognition & understanding. IEEE, 559--564","author":"Xinhui","unstructured":"Xinhui Zhou et al. 2011. Linear versus mel frequency cepstral coefficients for speaker recognition. In 2011 IEEE workshop on automatic speech recognition & understanding. IEEE, 559--564."}],"event":{"name":"CCS '23: ACM SIGSAC Conference on Computer and Communications Security","location":"Copenhagen Denmark","acronym":"CCS '23","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"]},"container-title":["Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3576915.3623209","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3576915.3623209","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3576915.3623209","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T01:54:28Z","timestamp":1755741268000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3576915.3623209"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,15]]},"references-count":67,"alternative-id":["10.1145\/3576915.3623209","10.1145\/3576915"],"URL":"https:\/\/doi.org\/10.1145\/3576915.3623209","relation":{},"subject":[],"published":{"date-parts":[[2023,11,15]]},"assertion":[{"value":"2023-11-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}