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Machine learning classifiers are known to be vulnerable to inputs maliciously constructed by adversaries to force misclassification.
Apprenticeship learning via inverse reinforcement learning
P. Abbeel and A. Y. Ng · 2004
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Can machine learning be secure?
M. Barreno, B. Nelson, R. Sears, A. D. Joseph, and J. D. Tygar · 2006
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Poisoning attacks against support vector machines
B. Biggio, B. Nelson, and L. Pavel · 2012
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The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
Earlier work this paper cites.
Evasion attacks against machine learning at test time
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Trust region policy optimization
J. Schulman, S. Levine, P. Moritz, M. I. Jordan, and P. Abbeel · 2015
Cited alongside, same era.
End to end learning for self-driving cars
M. Bojarski, D. D. Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba · 2016
Cited alongside, same era.
Openai gym, 2016
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
Cited alongside, same era.
Benchmarking deep reinforcement learning for continuous control
Y. Duan, X. Chen, R. Houthooft, J. Schulman, and P. Abbeel · 2016
Cited alongside, same era.
Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
V. Mnih, A. Puigdomenech Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
Later among the works it cites.
Practical black-box attacks against deep learning systems using adversarial examples
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2016
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Towards the science of security and privacy in machine learning
N. Papernot, P. McDaniel, A. Sinha, and M. Wellman · 2016
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Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter · 2016
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Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis · 2016
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S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2016
Cited alongside, same era.
Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2016
Cited alongside, same era.
Later among the works it cites.
Vulnerability of deep reinforcement learning to policy induction attacks
V. Behzadan and A. Munir · 2017
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Adversarial machine learning at scale
A. Kurakin, I. Goodfellow, and S. Bengio · 2017
Closest in time.