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Adversarial examples are inputs intentionally perturbed with the aim of forcing a machine learning model to produce a wrong prediction, while the changes are not easily detectable by a human.
Analysis of universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, and Stefano Soatto · 1911
Earlier work this paper cites.
The Scientist and Engineer’s Guide to Digital Signal Processing
Steven W Smith et al · 1997
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A normalized Levenshtein distance metric
Li Yujian and Liu Bo · 2007
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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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Deep speech: Scaling up end-to-end speech recognition
Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, et al · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Deepdriving: Learning affordance for direct perception in autonomous driving
Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Convolutional neural networks for small-footprint keyword spotting
Tara N Sainath and Carolina Parada · 2015
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
Earlier work this paper cites.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
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Deep speech 2: End-to-end speech recognition in english and mandarin
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, et al · 2016
Earlier work this paper cites.
Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Cited alongside, same era.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Cited alongside, same era.
Continuous authentication for voice assistants
Huan Feng, Kassem Fawaz, and Kang G Shin · 2017
Cited alongside, same era.
Voice biometrics: Deep learning-based voiceprint authentication system
Andrew Boles and Paul Rad · 2017
Cited alongside, same era.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
Cited alongside, same era.
Fast feature fool: A data independent approach to universal adversarial perturbations
Improving end-to-end speech recognition with policy learning
Yingbo Zhou, Caiming Xiong, and Richard Socher · 2018
Later among the works it cites.
Audio adversarial examples: Targeted attacks on speech-to-text
Nicholas Carlini and David Wagner · 2018
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Project deepspeech
Mozilla · 2018
Later among the works it cites.
Did you hear that? Adversarial examples against automatic speech recognition
Moustafa Alzantot, Bharathan Balaji, and Mani Srivastava · 2018
Later among the works it cites.
Speech commands: A dataset for limited-vocabulary speech recognition
Pete Warden · 2018
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Konda Reddy Mopuri, Utsav Garg, and R Venkatesh Babu · 2017
Cited alongside, same era.
Houdini: Fooling deep structured prediction models
Moustapha Cisse, Yossi Adi, Natalia Neverova, and Joseph Keshet · 2017
Cited alongside, same era.
An overview of vulnerabilities of voice controlled systems
Yuan Gong and Christian Poellabauer · 2018
Cited alongside, same era.
Idealised bayesian neural networks cannot have adversarial examples: Theoretical and empirical study
Yarin Gal and Lewis Smith · 2018
Cited alongside, same era.
Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal Mian · 2018
Cited alongside, same era.
Art of singular vectors and universal adversarial perturbations
Valentin Khrulkov and Ivan Oseledets · 2018
Cited alongside, same era.
Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
Later among the works it cites.
Universal adversarial perturbations for speech recognition systems
Paarth Neekhara, Shehzeen Hussain, Prakhar Pandey, Shlomo Dubnov, Julian McAuley, and Farinaz Koushanfar · 2019
Closest in time.
Universal adversarial audio perturbations
Sajjad Abdoli, Luiz G Hafemann, Jerome Rony, Ismail Ben Ayed, Patrick Cardinal, and Alessandro L Koerich · 2019
Closest in time.
Discriminative frequency filter banks learning with neural networks
Teng Zhang and Ji Wu · 2019
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Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses
Jérôme Rony, Luiz G Hafemann, Luiz S Oliveira, Ismail Ben Ayed, Robert Sabourin, and Eric Granger · 2019
Closest in time.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Closest in time.