ML-LOO: Detecting Adversarial Examples with Feature Attribution

8 June 2019

Papers citing "ML-LOO: Detecting Adversarial Examples with Feature Attribution"

50 / 59 papers shown

Title
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 80 454 0 27 Jan 2019
Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks T. Brunner Frederik Diehl Michael Truong-Le Alois Knoll MLAU AAML 75 117 0 24 Dec 2018
Concise Explanations of Neural Networks using Adversarial Training P. Chalasani Jiefeng Chen Aravind Sadagopan S. Jha Xi Wu AAML FAtt 118 13 0 15 Oct 2018
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network Xuanqing Liu Yao Li Chongruo Wu Cho-Jui Hsieh AAML OOD 62 171 0 01 Oct 2018
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan FAtt TDI 115 216 0 08 Aug 2018
Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors Andrew Ilyas Logan Engstrom Aleksander Madry MLAU AAML 97 375 0 20 Jul 2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks Kimin Lee Kibok Lee Honglak Lee Jinwoo Shin OODD 192 2,062 0 10 Jul 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 108 1,782 0 30 May 2018
Training verified learners with learned verifiers Krishnamurthy Dvijotham Sven Gowal Robert Stanforth Relja Arandjelović Brendan O'Donoghue J. Uesato Pushmeet Kohli OOD 65 169 0 25 May 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 155 795 0 30 Apr 2018
Black-box Adversarial Attacks with Limited Queries and Information Andrew Ilyas Logan Engstrom Anish Athalye Jessy Lin MLAU AAML 163 1,206 0 23 Apr 2018
On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples Pei-Hsuan Lu Pin-Yu Chen Chia-Mu Yu AAML 50 26 0 26 Mar 2018
Detecting Adversarial Perturbations with Saliency Chiliang Zhang Zhimou Yang Zuochang Ye AAML 31 32 0 23 Mar 2018
Certified Robustness to Adversarial Examples with Differential Privacy Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel J. Hsu Suman Jana SILM AAML 96 939 0 09 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 243 3,194 0 01 Feb 2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Xingjun Ma Yue Liu Yisen Wang S. Erfani S. Wijewickrema Grant Schoenebeck Basel Alomair Michael E. Houle James Bailey AAML 111 742 0 08 Jan 2018
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models Wieland Brendel Jonas Rauber Matthias Bethge AAML 69 1,350 0 12 Dec 2017
Towards Robust Neural Networks via Random Self-ensemble Xuanqing Liu Minhao Cheng Huan Zhang Cho-Jui Hsieh FedML AAML 101 422 0 02 Dec 2017
Provable defenses against adversarial examples via the convex outer adversarial polytope Eric Wong J. Zico Kolter AAML 128 1,504 0 02 Nov 2017
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples Yang Song Taesup Kim Sebastian Nowozin Stefano Ermon Nate Kushman AAML 112 790 0 30 Oct 2017
Interpretation of Neural Networks is Fragile Amirata Ghorbani Abubakar Abid James Zou FAtt AAML 133 870 0 29 Oct 2017
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 Cho-Jui Hsieh AAML 85 1,885 0 14 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 317 12,131 0 19 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,018 0 22 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 131 1,864 0 20 May 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,729 0 19 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 203 3,881 0 10 Apr 2017
Enhancing Robustness of Machine Learning Systems via Data Transformations A. Bhagoji Daniel Cullina Chawin Sitawarin Prateek Mittal AAML 56 231 0 09 Apr 2017
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks Weilin Xu David Evans Yanjun Qi AAML 87 1,271 0 04 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 193 6,018 0 04 Mar 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 93 894 0 01 Mar 2017
On the (Statistical) Detection of Adversarial Examples Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel AAML 78 714 0 21 Feb 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 65 950 0 14 Feb 2017
Understanding Neural Networks through Representation Erasure Jiwei Li Will Monroe Dan Jurafsky AAML MILM 97 567 0 24 Dec 2016
Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics Xin Li Fuxin Li GAN AAML 113 365 0 22 Dec 2016
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu Basel Alomair AAML 143 1,741 0 08 Nov 2016
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 472 3,148 0 04 Nov 2016
Robustness of classifiers: from adversarial to random noise Alhussein Fawzi Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 94 376 0 31 Aug 2016
A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples T. Tanay Lewis D. Griffin AAML 85 272 0 27 Aug 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 793 36,881 0 25 Aug 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 282 8,583 0 16 Aug 2016
Early Methods for Detecting Adversarial Images Dan Hendrycks Kevin Gimpel AAML 83 236 0 01 Aug 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 183 3,706 0 10 Jun 2016
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples Nicolas Papernot Patrick McDaniel Ian Goodfellow SILM AAML 116 1,741 0 24 May 2016
Identity Mappings in Deep Residual Networks Kaiming He Xinming Zhang Shaoqing Ren Jian Sun 354 10,196 0 16 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,033 0 16 Feb 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 75 3,682 0 08 Feb 2016
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 115 3,967 0 24 Nov 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 154 4,905 0 14 Nov 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 113 3,076 0 14 Nov 2015