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Deep neural networks have been shown to lack robustness to small input perturbations.
Can machine learning be secure?
Marco Barreno, Blaine Nelson, Russell Sears, Anthony D. Joseph, and J. D. Tygar · 2006
Earlier work this paper cites.
Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. 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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Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 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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Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick D. McDaniel, and Ian J. Goodfellow · 2016
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami · 2016
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Towards the science of security and privacy in machine learning
Nicolas Papernot, Patrick D. McDaniel, Arunesh Sinha, and Michael P. Wellman · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick D. McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Towards Verified Artificial Intelligence
Sanjit A. Seshia, Dorsa Sadigh, and S. Shankar Sastry · 2016
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Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
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Automatically evading classifiers: A case study on PDF malware classifiers
Weilin Xu, Yanjun Qi, and David Evans · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2017
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ZOO: zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
Cited alongside, same era.
Evading classifiers by morphing in the dark
Hung Dang, Yue Huang, and Ee-Chien Chang · 2017
Cited alongside, same era.
Compositional falsification of cyber-physical systems with machine learning components
Tommaso Dreossi, Alexandre Donzé, and Sanjit A. Seshia · 2017
Cited alongside, same era.
Systematic testing of convolutional neural networks for autonomous driving
Tommaso Dreossi, Shromona Ghosh, Alberto L. Sangiovanni-Vincentelli, and Sanjit A. Seshia · 2017
Cited alongside, same era.
Formal verification of piece-wise linear feed-forward neural networks
Rüdiger Ehlers · 2017
Cited alongside, same era.
Safety verification of deep neural networks
EAD: elastic-net attacks to deep neural networks via adversarial examples
Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh · 2018
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Counterexample-guided data augmentation
Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Kurt Keutzer, Alberto L. Sangiovanni-Vincentelli, and Sanjit A. Seshia · 2018
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Semantic adversarial deep learning
Tommaso Dreossi, Somesh Jha, and Sanjit A. Seshia · 2018
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Output range analysis for deep feedforward neural networks
Souradeep Dutta, Susmit Jha, Sriram Sankaranarayanan, and Ashish Tiwari · 2018
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A dual approach to scalable verification of deep networks
Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy A. Mann, and Pushmeet Kohli · 2018
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Making machine learning robust against adversarial inputs
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Xiaowei Huang, Marta Kwiatkowska, Sen Wang, and Min Wu · 2017
Cited alongside, same era.
Reluplex: An efficient SMT solver for verifying deep neural networks
Guy Katz, Clark W. Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
Cited alongside, same era.
Deepxplore: Automated whitebox testing of deep learning systems
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana · 2017
Cited alongside, same era.
Efficient defenses against adversarial attacks
Valentina Zantedeschi, Maria-Irina Nicolae, and Ambrish Rawat · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David A. Wagner · 2018
Cited alongside, same era.
Ian J. Goodfellow, Patrick D. McDaniel, and Nicolas Papernot · 2018
Later among the works it cites.
Reachability analysis of deep neural networks with provable guarantees
Wenjie Ruan, Xiaowei Huang, and Marta Kwiatkowska · 2018
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Formal specification for deep neural networks
Sanjit A. Seshia, Ankush Desai, Tommaso Dreossi, Daniel J. Fremont, Shromona Ghosh, Edward Kim, Sumukh Shivakumar, Marcell Vazquez-Chanlatte, and Xiangyu Yue · 2018
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Towards fast computation of certified robustness for relu networks
Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane S. Boning, and Inderjit S. Dhillon · 2018
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Evaluating the robustness of neural networks: An extreme value theory approach
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, and Luca Daniel · 2018
Later among the works it cites.
Feature-guided black-box safety testing of deep neural networks
Matthew Wicker, Xiaowei Huang, and Marta Kwiatkowska · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and J. Zico Kolter · 2018
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Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2018
Later among the works it cites.