v1v2 (latest)

Certified Adversarial Robustness via Randomized Smoothing

8 February 2019

Jeremy M. Cohen

Elan Rosenfeld

J. Zico Kolter

AAML

ArXiv (abs)PDF HTML Github (390★)

Papers citing "Certified Adversarial Robustness via Randomized Smoothing"

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Title
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 91 26 0 20 Mar 2021
Boosting Adversarial Transferability through Enhanced Momentum Xiaosen Wang Jiadong Lin Han Hu Jingdong Wang Kun He AAML 132 77 0 19 Mar 2021
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition Yaodong Yu Zitong Yang Yan Sun Jacob Steinhardt Yi-An Ma 87 17 0 17 Mar 2021
Improved, Deterministic Smoothing for L_1 Certified Robustness Alexander Levine Soheil Feizi AAML 103 47 0 17 Mar 2021
Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy Bronya R. Chernyak Bhiksha Raj Tamir Hazan Joseph Keshet AAML 69 1 0 15 Mar 2021
Adversarial Training is Not Ready for Robot Learning Mathias Lechner Ramin Hasani Radu Grosu Daniela Rus T. Henzinger AAML 100 34 0 15 Mar 2021
A Unified Game-Theoretic Interpretation of Adversarial Robustness Jie Ren Die Zhang Yisen Wang Lu Chen Zhanpeng Zhou ... Xu Cheng Xin Eric Wang Meng Zhou Jie Shi Quanshi Zhang AAML 136 23 0 12 Mar 2021
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling Md. Shohidul Islam Ihsen Alouani Khaled N. Khasawneh AAML 43 1 0 11 Mar 2021
Constrained Learning with Non-Convex Losses Luiz F. O. Chamon Santiago Paternain Miguel Calvo-Fullana Alejandro Ribeiro 109 38 0 08 Mar 2021
Consistency Regularization for Adversarial Robustness Jihoon Tack Sihyun Yu Jongheon Jeong Minseon Kim Sung Ju Hwang Jinwoo Shin AAML 120 61 0 08 Mar 2021
Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy Chong Chen Kezhi Kong Peihong Yu J. Luque Tom Goldstein Furong Huang AAML 82 8 0 07 Mar 2021
PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations Mark Niklas Muller Gleb Makarchuk Gagandeep Singh Markus Püschel Martin Vechev 112 94 0 05 Mar 2021
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack Mengting Xu Tao Zhang Zhongnian Li Mingxia Liu Daoqiang Zhang AAML OOD MedIm 105 45 0 05 Mar 2021
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples Washington Garcia Pin-Yu Chen S. Jha Scott Clouse Kevin R. B. Butler AAML 50 0 0 04 Mar 2021
PointGuard: Provably Robust 3D Point Cloud Classification Hongbin Liu Jinyuan Jia Neil Zhenqiang Gong 3DPC 123 76 0 04 Mar 2021
Smoothness Analysis of Adversarial Training Sekitoshi Kanai Masanori Yamada Hiroshi Takahashi Yuki Yamanaka Yasutoshi Ida AAML 106 6 0 02 Mar 2021
Adversarial training in communication constrained federated learning Devansh Shah Parijat Dube Supriyo Chakraborty Ashish Verma FedML 109 34 0 01 Mar 2021
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness Jacob D. Abernethy Pranjal Awasthi Satyen Kale AAML 68 6 0 01 Mar 2021
Explaining Adversarial Vulnerability with a Data Sparsity Hypothesis Mahsa Paknezhad Cuong Phuc Ngo Amadeus Aristo Winarto Alistair Cheong Beh Chuen Yang Wu Jiayang Lee Hwee Kuan OOD AAML 100 9 0 01 Mar 2021
Federated Learning without Revealing the Decision Boundaries Roozbeh Yousefzadeh FedML 27 0 0 01 Mar 2021
Adversarial Robustness with Non-uniform Perturbations Ece Naz Erdemir Jeffrey Bickford Luca Melis Sergul Aydore AAML 75 27 0 24 Feb 2021
On the robustness of randomized classifiers to adversarial examples Rafael Pinot Laurent Meunier Florian Yger Cédric Gouy-Pailler Y. Chevaleyre Jamal Atif AAML 82 14 0 22 Feb 2021
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations Sushant Agarwal S. Jabbari Chirag Agarwal Sohini Upadhyay Zhiwei Steven Wu Himabindu Lakkaraju FAtt AAML 95 64 0 21 Feb 2021
A PAC-Bayes Analysis of Adversarial Robustness Paul Viallard Guillaume Vidot Amaury Habrard Emilie Morvant AAML 80 15 0 19 Feb 2021
Center Smoothing: Certified Robustness for Networks with Structured Outputs Aounon Kumar Tom Goldstein OOD AAML UQCV 84 19 0 19 Feb 2021
Bridging the Gap Between Adversarial Robustness and Optimization Bias Fartash Faghri Sven Gowal C. N. Vasconcelos David J. Fleet Fabian Pedregosa Nicolas Le Roux AAML 234 7 0 17 Feb 2021
Globally-Robust Neural Networks Klas Leino Zifan Wang Matt Fredrikson AAML OOD 165 131 0 16 Feb 2021
Low Curvature Activations Reduce Overfitting in Adversarial Training Vasu Singla Sahil Singla David Jacobs Soheil Feizi AAML 110 47 0 15 Feb 2021
Certified Robustness to Programmable Transformations in LSTMs Yuhao Zhang Aws Albarghouthi Loris Dántoni AAML 79 22 0 15 Feb 2021
Certifiably Robust Variational Autoencoders Ben Barrett A. Camuto M. Willetts Tom Rainforth AAML DRL 90 17 0 15 Feb 2021
And/or trade-off in artificial neurons: impact on adversarial robustness A. Fontana AAML 62 0 0 15 Feb 2021
Generating Structured Adversarial Attacks Using Frank-Wolfe Method Ehsan Kazemi Thomas Kerdreux Liquang Wang AAML DiffM 61 1 0 15 Feb 2021
Perceptually Constrained Adversarial Attacks Muhammad Zaid Hameed András Gyorgy 76 13 0 14 Feb 2021
Connecting Interpretability and Robustness in Decision Trees through Separation Michal Moshkovitz Yao-Yuan Yang Kamalika Chaudhuri 82 23 0 14 Feb 2021
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections A. Camuto Xiaoyu Wang Lingjiong Zhu Chris Holmes Mert Gurbuzbalaban Umut Simsekli 76 16 0 13 Feb 2021
Mixed Nash Equilibria in the Adversarial Examples Game Laurent Meunier M. Scetbon Rafael Pinot Jamal Atif Y. Chevaleyre AAML 91 30 0 13 Feb 2021
On the Paradox of Certified Training Nikola Jovanović Mislav Balunović Maximilian Baader Martin Vechev OOD 108 13 0 12 Feb 2021
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective Chaoning Zhang Philipp Benz Adil Karjauv In So Kweon AAML 116 42 0 12 Feb 2021
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons Bohang Zhang Tianle Cai Zhou Lu Di He Liwei Wang OOD 97 51 0 10 Feb 2021
Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers Jacob Mitchell Springer Melanie Mitchell Garrett Kenyon AAML 67 13 0 09 Feb 2021
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples Jay Nandy Sudipan Saha Wynne Hsu Mong Li Lee Xiaosu Zhu AAML 89 4 0 09 Feb 2021
Mask-GVAE: Blind Denoising Graphs via Partition Jia Li Mengzhou Liu Honglei Zhang Pengyun Wang Yong Wen Lujia Pan Hong Cheng 82 9 0 08 Feb 2021
Efficient Certified Defenses Against Patch Attacks on Image Classifiers J. H. Metzen Maksym Yatsura AAML 61 41 0 08 Feb 2021
Adversarial example generation with AdaBelief Optimizer and Crop Invariance Bo Yang Hengwei Zhang Yuchen Zhang Kaiyong Xu Jin-dong Wang AAML 76 29 0 07 Feb 2021
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks Chong Xiang Prateek Mittal AAML 128 53 0 05 Feb 2021
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression Masanori Yamada Sekitoshi Kanai Tomoharu Iwata Tomokatsu Takahashi Yuki Yamanaka Hiroshi Takahashi Atsutoshi Kumagai AAML 129 9 0 05 Feb 2021
Adversarially Robust Learning with Unknown Perturbation Sets Omar Montasser Steve Hanneke Nathan Srebro AAML 93 28 0 03 Feb 2021
Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems Alireza Bahramali Milad Nasr Amir Houmansadr Dennis Goeckel Don Towsley AAML 87 58 0 01 Feb 2021
Admix: Enhancing the Transferability of Adversarial Attacks Xiaosen Wang Xu He Jingdong Wang Kun He AAML 159 201 0 31 Jan 2021
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks Daniel Schwalbe-Koda Aik Rui Tan Rafael Gómez-Bombarelli AAML 110 63 0 27 Jan 2021