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Despite remarkable improvements, automatic speech recognition is susceptible to adversarial perturbations.
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Eberhard Zwicker and Hugo Fastl · 2007
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Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Intriguing Properties of Neural Networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Cocaine noodles: exploiting the gap between human and machine speech recognition
Tavish Vaidya, Yuankai Zhang, Micah Sherr, and Clay Shields · 2015
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Hidden voice commands
Nicholas Carlini, Pratyush Mishra, Tavish Vaidya, Yuankai Zhang, Micah Sherr, Clay Shields, David Wagner, and Wenchao Zhou · 2016
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Convolutional recurrent neural networks for small-footprint keyword spotting
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
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Audio adversarial examples: Targeted attacks on speech-to-text
Nicholas Carlini and David Wagner · 2018
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Stronger data poisoning attacks break data sanitization defenses
Pang Wei Koh, Jacob Steinhardt, and Percy Liang · 2018
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Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
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Efficient keyword spotting using time delay neural networks
Samuel Myer and Vikrant Singh Tomar · 2018
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Adversarial attacks against automatic speech recognition systems via psychoacoustic hiding
Lea Schönherr, Katharina Kohls, Steffen Zeiler, Thorsten Holz, and Dorothea Kolossa · 2018
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Poison frogs! Targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
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Attention-based end-to-end models for small-footprint keyword spotting
Changhao Shan, Junbo Zhang, Yujun Wang, and Lei Xie · 2018
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Detecting adversarial examples for speech recognition via uncertainty quantification
Sina Däubener, Lea Schönherr, Asja Fischer, and Dorothea Kolossa · 2020
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Witches’ brew: Industrial scale data poisoning via gradient matching
Jonas Geiping, Liam Fowl, W Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, and Tom Goldstein · 2020
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Metapoison: Practical general-purpose clean-label data poisoning
W Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, and Tom Goldstein · 2020
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Adversarial machine learning-industry perspectives
Ram Shankar Siva Kumar, Magnus Nyström, John Lambert, Andrew Marshall, Mario Goertzel, Andi Comissoneru, Matt Swann, and Sharon Xia · 2020
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Deep k-nn defense against clean-label data poisoning attacks
Neehar Peri, Neal Gupta, W Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, and John P Dickerson · 2020
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Speech commands: A dataset for limited-vocabulary speech recognition
Pete Warden · 2018
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Analyzing federated learning through an adversarial lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2019
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Deep learning poison data attack detection
Henry Chacon, Samuel Silva, and Paul Rad · 2019
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Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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Imperceptible, robust, and targeted adversarial examples for automatic speech recognition
Yao Qin, Nicholas Carlini, Garrison Cottrell, Ian Goodfellow, and Colin Raffel · 2019
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Building and evaluation of a real room impulse response dataset
Igor Szöke, Miroslav Skácel, Ladislav Mošner, Jakub Paliesek, and Jan Černockỳ · 2019
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An overview of end-to-end automatic speech recognition
Dong Wang, Xiaodong Wang, and Shaohe Lv · 2019
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Imperio: Robust over-the-air adversarial examples for automatic speech recognition systems
Lea Schönherr, Thorsten Eisenhofer, Steffen Zeiler, Thorsten Holz, and Dorothea Kolossa · 2020
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Data poisoning attacks against federated learning systems
Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy, and Ling Liu · 2020
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Dataset security for machine learning: Data poisoning, backdoor attacks, and defensess
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, and Tom Goldstein · 2021
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Can you hear it? backdoor attacks via ultrasonic triggers
Stefanos Koffas, Jing Xu, Mauro Conti, and Stjepan Picek · 2021
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SpeechBrain: A general-purpose speech toolkit, 2021
Mirco Ravanelli, Titouan Parcollet, Peter Plantinga, Aku Rouhe, Samuele Cornell, Loren Lugosch, Cem Subakan, Nauman Dawalatabad, Abdelwahab Heba, Jianyuan Zhong, Ju-Chieh Chou, Sung-Lin Yeh, Szu-Wei Fu, Chien-Feng Liao, Elena Rastorgueva, François Grondin, William Aris, Hwidong Na, Yan Gao, Renato De Mori, and Yoshua Bengio · 2021
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Just how toxic is data poisoning? a unified benchmark for backdoor and data poisoning attacks
Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P Dickerson, and Tom Goldstein · 2021
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Poison forensics: Traceback of data poisoning attacks in neural networks
Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, and Ben Y. Zhao · 2022
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On-device voice control on sonos speakers, May 2022
David Leroy Alice Coucke, Joseph Dureau and Sébastien Maury · 2026
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Python rir simulator, October 2021
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Carnegie Mellon Pronouncing Dictionary (CMUdict) - Version 0.7b, November 2014
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Number of digital voice assistants in use worldwide from 2019 to 2024, April 2020
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