Jacobian Norm with Selective Input Gradient Regularization for Improved and Interpretable Adversarial Defense

9 July 2022

Lin Wu

Xianghua Xie

Papers citing "Jacobian Norm with Selective Input Gradient Regularization for Improved and Interpretable Adversarial Defense"

24 / 24 papers shown

Title
Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability Jinwei Xing Takashi Nagata Xinyun Zou Emre Neftci J. Krichmar AAML 50 4 0 18 May 2022
Attack to Fool and Explain Deep Networks Naveed Akhtar M. Jalwana Bennamoun Ajmal Mian AAML 74 33 0 20 Jun 2021
Robust Learning with Jacobian Regularization Judy Hoffman Daniel A. Roberts Sho Yaida OOD AAML 54 167 0 07 Aug 2019
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training Haichao Zhang Jianyu Wang AAML 64 230 0 24 Jul 2019
On the Connection Between Adversarial Robustness and Saliency Map Interpretability Christian Etmann Sebastian Lunz Peter Maass Carola-Bibiane Schönlieb AAML FAtt 58 162 0 10 May 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 89 1,838 0 06 May 2019
Adversarial Training for Free! Ali Shafahi Mahyar Najibi Amin Ghiasi Zheng Xu John P. Dickerson Christoph Studer L. Davis Gavin Taylor Tom Goldstein AAML 130 1,249 0 29 Apr 2019
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack Adnan Siraj Rakin Zhezhi He Deliang Fan AAML 65 292 0 22 Nov 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 102 1,781 0 30 May 2018
Certified Robustness to Adversarial Examples with Differential Privacy Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel J. Hsu Suman Jana SILM AAML 96 934 0 09 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 224 3,186 0 01 Feb 2018
Towards Robust Neural Networks via Random Self-ensemble Xuanqing Liu Minhao Cheng Huan Zhang Cho-Jui Hsieh FedML AAML 90 421 0 02 Dec 2017
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 147 682 0 26 Nov 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 80 1,879 0 14 Aug 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,725 0 19 May 2017
The Space of Transferable Adversarial Examples Florian Tramèr Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML SILM 90 557 0 11 Apr 2017
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu D. Song AAML 140 1,737 0 08 Nov 2016
Gradients of Counterfactuals Mukund Sundararajan Ankur Taly Qiqi Yan FAtt 56 104 0 08 Nov 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 264 8,555 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 540 5,897 0 08 Jul 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 75 3,678 0 08 Feb 2016
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 270 14,927 1 21 Dec 2013
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,295 0 20 Dec 2013
How to Explain Individual Classification Decisions D. Baehrens T. Schroeter Stefan Harmeling M. Kawanabe K. Hansen K. Müller FAtt 132 1,104 0 06 Dec 2009