DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks

2 October 2017

Papers citing "DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks"

5 / 5 papers shown

Title
Simplifying Neural Networks using Formal Verification S. Gokulanathan Alexander Feldsher Adi Malca Clark W. Barrett Guy Katz 6 4 0 25 Oct 2019
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 213 1,714 0 03 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 253 2,899 0 04 Nov 2016
Safety Verification of Deep Neural Networks Xiaowei Huang M. Kwiatkowska Sen Wang Min Wu AAML 164 883 0 21 Oct 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 245 5,361 0 08 Jul 2016