Towards Evaluating the Robustness of Neural Networks

16 August 2016

Papers citing "Towards Evaluating the Robustness of Neural Networks"

21 / 1,721 papers shown

Title
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 61 1,847 0 20 May 2017
MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense Sailik Sengupta Tathagata Chakraborti S. Kambhampati AAML 29 63 0 19 May 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 94 2,704 0 19 May 2017
DeepXplore: Automated Whitebox Testing of Deep Learning Systems Kexin Pei Yinzhi Cao Junfeng Yang Suman Jana AAML 48 1,354 0 18 May 2017
Extending Defensive Distillation Nicolas Papernot Patrick McDaniel AAML 32 118 0 15 May 2017
DeepCorrect: Correcting DNN models against Image Distortions Tejas S. Borkar Lina Karam 27 94 0 05 May 2017
Universal Adversarial Perturbations Against Semantic Image Segmentation J. H. Metzen Mummadi Chaithanya Kumar Thomas Brox Volker Fischer AAML 30 287 0 19 Apr 2017
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks Weilin Xu David Evans Yanjun Qi AAML 25 1,237 0 04 Apr 2017
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems Hossein Hosseini Yize Chen Sreeram Kannan Baosen Zhang Radha Poovendran AAML 30 106 0 13 Mar 2017
Dropout Inference in Bayesian Neural Networks with Alpha-divergences Yingzhen Li Y. Gal UQCV BDL 51 196 0 08 Mar 2017
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents Yen-Chen Lin Zhang-Wei Hong Yuan-Hong Liao Meng-Li Shih Ming Liu Min Sun AAML 28 411 0 08 Mar 2017
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components T. Dreossi Alexandre Donzé S. Seshia AAML 32 230 0 02 Mar 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 36 891 0 01 Mar 2017
On the (Statistical) Detection of Adversarial Examples Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel AAML 39 709 0 21 Feb 2017
Deep Reinforcement Learning: An Overview Yuxi Li OffRL VLM 109 1,505 0 25 Jan 2017
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks Vahid Behzadan Arslan Munir AAML SILM 26 274 0 16 Jan 2017
Learning Adversary-Resistant Deep Neural Networks Qinglong Wang Wenbo Guo Kaixuan Zhang Alexander Ororbia Masashi Sugiyama Xue Liu C. Lee Giles AAML 23 43 0 05 Dec 2016
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu D. Song AAML 78 1,723 0 08 Nov 2016
Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics J. M. Wong 29 11 0 01 Nov 2016
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 183 932 0 21 Oct 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 368 5,849 0 08 Jul 2016