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Recent works have developed several methods of defending neural networks against adversarial attacks with certified guarantees.
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
Salman, H.; Yang, G.; Zhang, H.; Hsieh, C.-J.; and Zhang, P. 2019 · 1902
Earlier work this paper cites.
POPQORN: Quantifying robustness of recurrent neural networks
Ko, C.-Y.; Lyu, Z.; Weng, T.-W.; Daniel, L.; Wong, N.; and Lin, D. 2019 · 1905
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I.; and Fergus, R. 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D.; and Ba, J. 2014 · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2015 · 2015
Earlier work this paper cites.
TensorFlow: A System for Large-Scale Machine Learning
Abadi, M.; Barham, P.; Chen, J.; Chen, Z.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Irving, G.; Isard, M.; et al. 2016 · 2016
Earlier work this paper cites.
The limitations of deep learning in adversarial settings
Papernot, N.; McDaniel, P.; Jha, S.; Fredrikson, M.; Celik, Z. B.; and Swami, A. 2016 · 2016
Earlier work this paper cites.
Synthesizing robust adversarial examples
Athalye, A.; and Sutskever, I. 2017 · 2017
Earlier work this paper cites.
Provably Minimally-Distorted Adversarial Examples
Carlini, N.; Katz, G.; Barrett, C.; and Dill, D. L. 2017 · 2017
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Carlini, N.; and Wagner, D. 2017 · 2017
Earlier work this paper cites.
Maximum resilience of artificial neural networks
Cheng, C.-H.; Nührenberg, G.; and Ruess, H. 2017 · 2017
Earlier work this paper cites.
Formal guarantees on the robustness of a classifier against adversarial manipulation
Hein, M.; and Andriushchenko, M. 2017 · 2017
Earlier work this paper cites.
Reluplex: An efficient SMT solver for verifying deep neural networks
Katz, G.; Barrett, C.; Dill, D. L.; Julian, K.; and Kochenderfer, M. J. 2017 · 2017
Cited alongside, same era.
Lower bounds on the robustness to adversarial perturbations
Peck, J.; Roels, J.; Goossens, B.; and Saeys, Y. 2017 · 2017
Cited alongside, same era.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Athalye, A.; Carlini, N.; and Wagner, D. 2018 · 2018
Cited alongside, same era.
EAD: elastic-net attacks to deep neural networks via adversarial examples
Chen, P.-Y.; Sharma, Y.; Zhang, H.; Yi, J.; and Hsieh, C.-J. 2018 · 2018
Cited alongside, same era.
AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation
Gehr, T.; Mirman, M.; Drachsler-Cohen, D.; Tsankov, P.; Chaudhuri, S.; and Vechev, M. 2018 · 2018
Cited alongside, same era.
Towards Fast Computation of Certified Robustness for ReLU Networks
Weng, T.-W.; Zhang, H.; Chen, H.; Song, Z.; Hsieh, C.-J.; Boning, D.; Dhillon, I. S.; and Daniel, L. 2018 · 2018
Later among the works it cites.
Scaling provable adversarial defenses
Wong, E.; Schmidt, F.; Metzen, J. H.; and Kolter, J. Z. 2018 · 2018
Later among the works it cites.
Structured adversarial attack: Towards general implementation and better interpretability
Xu, K.; Liu, S.; Zhao, P.; Chen, P.-Y.; Zhang, H.; Erdogmus, D.; Wang, Y.; and Lin, X. 2018 · 2018
Later among the works it cites.
Efficient Neural Network Robustness Certification with General Activation Functions
Zhang, H.; Weng, T.-W.; Chen, P.-Y.; Hsieh, C.-J.; and Daniel, L. 2018 · 2018
Later among the works it cites.
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
Boopathy, A.; Weng, T.-W.; Chen, P.-Y.; Liu, S.; and Daniel, L. 2019 · 2019
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Kolter, J. Z.; and Wong, E. 2018 · 2018
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2018 · 2018
Cited alongside, same era.
Differentiable abstract interpretation for provably robust neural networks
Mirman, M.; Gehr, T.; and Vechev, M. 2018 · 2018
Cited alongside, same era.
Certified defenses against adversarial examples
Raghunathan, A.; Steinhardt, J.; and Liang, P. 2018 · 2018
Cited alongside, same era.
Fast and Effective Robustness Certification
Singh, G.; Gehr, T.; Mirman, M.; Püschel, M.; and Vechev, M. 2018 · 2018
Cited alongside, same era.
Certifiable Distributional Robustness with Principled Adversarial Training
Sinha, A.; Namkoong, H.; and Duchi, J. 2018 · 2018
Cited alongside, same era.
Efficient Formal Safety Analysis of Neural Networks
Wang, S.; Pei, K.; Whitehouse, J.; Yang, J.; and Jana, S. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
Scalable Verified Training for Provably Robust Image Classification
Gowal, S.; Dvijotham, K.; Stanforth, R.; Bunel, R.; Qin, C.; Uesato, J.; Arandjelovic, R.; Mann, T. A.; and Kohli, P. 2019 · 2019
Later among the works it cites.
An Abstract Domain for Certifying Neural Networks
Singh, G.; Gehr, T.; Mirman, M.; Püschel, M.; and Vechev, M. 2019 · 2019
Later among the works it cites.
One pixel attack for fooling deep neural networks
Su, J.; Vargas, D. V.; and Sakurai, K. 2019 · 2019
Later among the works it cites.
Verifying Neural Networks with Mixed Integer Programming
Tjeng, V.; and Tedrake, R. 2019 · 2019
Later among the works it cites.
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability
Xiao, K. Y.; Tjeng, V.; Shafiullah, N. M.; and Madry, A. 2019 · 2019
Later among the works it cites.
Theoretically Principled Trade-off between Robustness and Accuracy
Zhang, H.; Yu, Y.; Jiao, J.; Xing, E. P.; El Ghaoui, L.; and Jordan, M. I. 2019 · 2019
Later among the works it cites.
Towards Stable and Efficient Training of Verifiably Robust Neural Networks
Zhang, H.; Chen, H.; Xiao, C.; Li, B.; Boning, D. S.; and Hsieh, C. 2020 · 2020
Later among the works it cites.