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Although end-to-end automatic speech recognition (e2e ASR) models are widely deployed in many applications, there have been very few studies to understand models' robustness against adversarial perturbations.
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,” in Proceedings of the 23rd international conference on Machine learning , 2006, pp. 369–376
2006
Earlier work this paper cites.
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: an asr corpus based on public domain audio books,” in 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2015, pp. 5206–5210
2015
Earlier work this paper cites.
N. Carlini, P. Mishra, T. Vaidya, Y. Zhang, M. Sherr, C. Shields, D. Wagner, and W. Zhou, “Hidden voice commands,” in 25th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 16) , 2016, pp. 513–530
2016
Earlier work this paper cites.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in 2017 ieee symposium on security and privacy (sp) . IEEE, 2017, pp. 39–57
2017
Earlier work this paper cites.
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard, “Universal adversarial perturbations,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1765–1773
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2019
Later among the works it cites.
Y. Qin, N. Carlini, G. Cottrell, I. Goodfellow, and C. Raffel, “Imperceptible, robust, and targeted adversarial examples for automatic speech recognition,” in International Conference on Machine Learning . PMLR, 2019, pp. 5231–5240
2019
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2019
Later among the works it cites.
2019
Later among the works it cites.
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2018
Cited alongside, same era.
2018
Cited alongside, same era.
N. Carlini and D. Wagner, “Audio adversarial examples: Targeted attacks on speech-to-text,” in 2018 IEEE Security and Privacy Workshops (SPW) . IEEE, 2018, pp. 1–7
2018
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Later among the works it cites.
Y. Xie, C. Shi, Z. Li, J. Liu, Y. Chen, and B. Yuan, “Real-time, universal, and robust adversarial attacks against speaker recognition systems,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 1738–1742
2020
Later among the works it cites.
H. Abdullah, K. Warren, V. Bindschaedler, N. Papernot, and P. Traynor, “Sok: The faults in our asrs: An overview of attacks against automatic speech recognition and speaker identification systems,” arXiv e-prints , pp. arXiv–2007, 2020
2020
Later among the works it cites.