Why Does Little Robustness Help? Understanding and Improving Adversarial Transferability from Surrogate Training

15 July 2023

Xiaogeng Liu

Papers citing "Why Does Little Robustness Help? Understanding and Improving Adversarial Transferability from Surrogate Training"

30 / 80 papers shown

Title
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks Wei-An Lin Chun Pong Lau Alexander Levine Ramalingam Chellappa Soheil Feizi AAML 83 60 0 05 Sep 2020
Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman Andrew Ilyas Logan Engstrom Ashish Kapoor Aleksander Madry 55 423 0 16 Jul 2020
Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability Kaizhao Liang Jacky Y. Zhang Wei Ping Zhuolin Yang Oluwasanmi Koyejo Yangqiu Song AAML 67 26 0 25 Jun 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 85 796 0 26 Feb 2020
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets Dongxian Wu Yisen Wang Shutao Xia James Bailey Xingjun Ma AAML SILM 57 312 0 14 Feb 2020
Jacobian Adversarially Regularized Networks for Robustness Alvin Chan Yi Tay Yew-Soon Ong Jie Fu AAML 57 74 0 21 Dec 2019
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration Kanil Patel William H. Beluch Dan Zhang Michael Pfeiffer Bin Yang UQCV 54 30 0 16 Dec 2019
RandAugment: Practical automated data augmentation with a reduced search space E. D. Cubuk Barret Zoph Jonathon Shlens Quoc V. Le MQ 195 3,458 0 30 Sep 2019
Robust Learning with Jacobian Regularization Judy Hoffman Daniel A. Roberts Sho Yaida OOD AAML 41 166 0 07 Aug 2019
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features Sangdoo Yun Dongyoon Han Seong Joon Oh Sanghyuk Chun Junsuk Choe Y. Yoo OOD 592 4,735 0 13 May 2019
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks Yinpeng Dong Tianyu Pang Hang Su Jun Zhu SILM AAML 75 836 0 05 Apr 2019
Learning Transferable Adversarial Examples via Ghost Networks Yingwei Li S. Bai Yuyin Zhou Cihang Xie Zhishuai Zhang Alan Yuille AAML 71 136 0 09 Dec 2018
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 241 279 0 03 Dec 2018
Characterizing and Avoiding Negative Transfer Zirui Wang Zihang Dai Barnabás Póczós J. Carbonell 80 413 0 24 Nov 2018
On the Geometry of Adversarial Examples Marc Khoury Dylan Hadfield-Menell AAML 46 79 0 01 Nov 2018
Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization Daniel Jakubovitz Raja Giryes AAML 39 210 0 23 Mar 2018
Improving Transferability of Adversarial Examples with Input Diversity Cihang Xie Zhishuai Zhang Yuyin Zhou Song Bai Jianyu Wang Zhou Ren Alan Yuille AAML 83 1,108 0 19 Mar 2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Xingjun Ma Yue Liu Yisen Wang S. Erfani S. Wijewickrema Grant Schoenebeck D. Song Michael E. Houle James Bailey AAML 95 734 0 08 Jan 2018
Gradient Regularization Improves Accuracy of Discriminative Models D. Varga Adrián Csiszárik Zsolt Zombori 35 53 0 28 Dec 2017
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 145 679 0 26 Nov 2017
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples Yang Song Taesup Kim Sebastian Nowozin Stefano Ermon Nate Kushman AAML 102 787 0 30 Oct 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 258 9,687 0 25 Oct 2017
Improved Regularization of Convolutional Neural Networks with Cutout Terrance Devries Graham W. Taylor 99 3,739 0 15 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 241 11,962 0 19 Jun 2017
Spectral Norm Regularization for Improving the Generalizability of Deep Learning Yuichi Yoshida Takeru Miyato 73 328 0 31 May 2017
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu D. Song AAML 133 1,727 0 08 Nov 2016
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens Z. Wojna 3DV BDL 575 27,231 0 02 Dec 2015
How transferable are features in deep neural networks? J. Yosinski Jeff Clune Yoshua Bengio Hod Lipson OOD 171 8,309 0 06 Nov 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 204 14,831 1 21 Dec 2013
Estimating the Hessian by Back-propagating Curvature James Martens Ilya Sutskever Kevin Swersky 73 80 0 27 Jun 2012