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In recent years, many efforts have demonstrated that modern machine learning algorithms are vulnerable to adversarial attacks, where small, but carefully crafted, perturbations on the input can make them fail.
Robot learning from demonstration
Christopher G Atkeson and Stefan Schaal · 1997
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces
Rainer Storn and Kenneth Price · 1997
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Introduction to reinforcement learning
Richard S Sutton, Andrew G Barto, et al · 1998
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
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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Convolutional neural networks for small-footprint keyword spotting
Tara Sainath and Carolina Parada · 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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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Houdini: Fooling deep structured prediction models
Moustapha Cisse, Yossi Adi, Natalia Neverova, and Joseph Keshet · 2017
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Hotflip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou · 2017
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Crafting adversarial examples for speech paralinguistics applications
Yuan Gong and Christian Poellabauer · 2017
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An overview of vulnerabilities of voice controlled systems
Yuan Gong and Christian Poellabauer · 2018
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Adversarial perturbations against real-time video classification systems
Shasha Li, Ajaya Neupane, Sujoy Paul, Chengyu Song, Srikanth V Krishnamurthy, Amit K Roy Chowdhury, and Ananthram Swami · 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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Speech commands: A dataset for limited-vocabulary speech recognition
Pete Warden · 2018
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Adversarial examples for malware detection
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick McDaniel · 2017
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
Cited alongside, same era.
Did you hear that? adversarial examples against automatic speech recognition
Moustafa Alzantot, Bharathan Balaji, and Mani Srivastava · 2018
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Audio adversarial examples: Targeted attacks on speech-to-text
Nicholas Carlini and David Wagner · 2018
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Hiromu Yakura and Jun Sakuma · 2018
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Universal adversarial perturbations for speech recognition systems
Paarth Neekhara, Shehzeen Hussain, Prakhar Pandey, Shlomo Dubnov, Julian McAuley, and Farinaz Koushanfar · 2019
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Imperceptible, robust, and targeted adversarial examples for automatic speech recognition
Yao Qin, Nicholas Carlini, Ian Goodfellow, Garrison Cottrell, and Colin Raffel · 2019
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One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai · 2019
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