Improving Robustness using Generated Data

18 October 2021

Sven Gowal

Sylvestre-Alvise Rebuffi

Papers citing "Improving Robustness using Generated Data"

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Title
Adversarial Training Should Be Cast as a Non-Zero-Sum Game Alexander Robey Fabian Latorre George J. Pappas Hamed Hassani V. Cevher AAML 66 12 0 19 Jun 2023
Wasserstein distributional robustness of neural networks Xingjian Bai Guangyi He Yifan Jiang J. Obłój OOD AAML 16 6 0 16 Jun 2023
Towards Understanding Clean Generalization and Robust Overfitting in Adversarial Training Binghui Li Yuanzhi Li AAML 26 3 0 02 Jun 2023
On the Importance of Backbone to the Adversarial Robustness of Object Detectors Xiao-Li Li Hang Chen Xiaolin Hu AAML 38 4 0 27 May 2023
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score Shuhai Zhang Feng Liu Jiahao Yang Yifan Yang Changsheng Li Bo Han Mingkui Tan DiffM AAML 34 17 0 25 May 2023
Training on Thin Air: Improve Image Classification with Generated Data Yongchao Zhou Hshmat Sahak Jimmy Ba DiffM 19 43 0 24 May 2023
Decoupled Kullback-Leibler Divergence Loss Jiequan Cui Zhuotao Tian Zhisheng Zhong Xiaojuan Qi Bei Yu Hanwang Zhang 39 38 0 23 May 2023
DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection Jiang-Long Liu Chun Pong Lau Ramalingam Chellappa DiffM 34 31 0 23 May 2023
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses Joana Cabral Costa Tiago Roxo Hugo Manuel Proença Pedro R. M. Inácio AAML 37 50 0 18 May 2023
Raising the Bar for Certified Adversarial Robustness with Diffusion Models Thomas Altstidl David Dobre Björn Eskofier Gauthier Gidel Leo Schwinn DiffM 34 7 0 17 May 2023
Utility Theory of Synthetic Data Generation Shi Xu W. Sun Guang Cheng 25 5 0 17 May 2023
The Best Defense is Attack: Repairing Semantics in Textual Adversarial Examples Heng Yang Ke Li AAML 27 2 0 06 May 2023
Synthetic Data from Diffusion Models Improves ImageNet Classification Shekoofeh Azizi Simon Kornblith Chitwan Saharia Mohammad Norouzi David J. Fleet VLM DiffM 40 292 0 17 Apr 2023
Improving Fast Adversarial Training with Prior-Guided Knowledge Xiaojun Jia Yong Zhang Xingxing Wei Baoyuan Wu Ke Ma Jue Wang Xiaochun Cao AAML 23 26 0 01 Apr 2023
SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection Jingyang Zhang Nathan Inkawhich Randolph Linderman R. Luley Yiran Chen H. Li OODD 16 1 0 25 Mar 2023
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing Lin Li Michael W. Spratling AAML 64 4 0 24 Mar 2023
An Extended Study of Human-like Behavior under Adversarial Training Paul Gavrikov J. Keuper M. Keuper AAML 28 9 0 22 Mar 2023
TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization Ziquan Liu Yi Tian Xu Xiangyang Ji Antoni B. Chan AAML 24 17 0 20 Mar 2023
Randomized Adversarial Training via Taylor Expansion Gao Jin Xinping Yi Dengyu Wu Ronghui Mu Xiaowei Huang AAML 41 34 0 19 Mar 2023
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness Peiyu Xiong Michael W. Tegegn Jaskeerat Singh Sarin Shubhraneel Pal Julia Rubin SILM AAML 32 8 0 17 Mar 2023
Robust Evaluation of Diffusion-Based Adversarial Purification M. Lee Dongwoo Kim 34 53 0 16 Mar 2023
Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases Aengus Lynch G. Dovonon Jean Kaddour Ricardo M. A. Silva 189 30 0 09 Mar 2023
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models Naman D. Singh Francesco Croce Matthias Hein OOD 39 62 0 03 Mar 2023
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks Sihui Dai Saeed Mahloujifar Chong Xiang Vikash Sehwag Pin-Yu Chen Prateek Mittal AAML OOD 23 7 0 21 Feb 2023
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker Sihui Dai Wen-Luan Ding A. Bhagoji Daniel Cullina Ben Y. Zhao Haitao Zheng Prateek Mittal AAML 27 2 0 21 Feb 2023
Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts Francesco Croce Sylvestre-Alvise Rebuffi Evan Shelhamer Sven Gowal AAML 34 17 0 20 Feb 2023
Better Diffusion Models Further Improve Adversarial Training Zekai Wang Tianyu Pang Chao Du Min-Bin Lin Weiwei Liu Shuicheng Yan DiffM 24 208 0 09 Feb 2023
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness Yuancheng Xu Yanchao Sun Micah Goldblum Tom Goldstein Furong Huang AAML 23 37 0 06 Feb 2023
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks Salah Ghamizi Jingfeng Zhang Maxime Cordy Mike Papadakis Masashi Sugiyama Yves Le Traon AAML 19 2 0 06 Feb 2023
Leaving Reality to Imagination: Robust Classification via Generated Datasets Hritik Bansal Aditya Grover OOD 44 87 0 05 Feb 2023
Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applications Chengyu Dong OOD UQCV BDL AI4CE 36 0 0 02 Feb 2023
Uncovering Adversarial Risks of Test-Time Adaptation Tong Wu Feiran Jia Xiangyu Qi Jiachen T. Wang Vikash Sehwag Saeed Mahloujifar Prateek Mittal AAML TTA 29 9 0 29 Jan 2023
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing Yatong Bai Brendon G. Anderson Aerin Kim Somayeh Sojoudi AAML 30 18 0 29 Jan 2023
Unlocking Deterministic Robustness Certification on ImageNet Kaiqin Hu Andy Zou Zifan Wang Klas Leino Matt Fredrikson OOD 21 12 0 29 Jan 2023
Data Augmentation Alone Can Improve Adversarial Training Lin Li Michael W. Spratling 16 50 0 24 Jan 2023
Provable Unrestricted Adversarial Training without Compromise with Generalizability Lili Zhang Ning Yang Yanchao Sun Philip S. Yu AAML 22 2 0 22 Jan 2023
Revisiting Residual Networks for Adversarial Robustness: An Architectural Perspective Shihua Huang Zhichao Lu Kalyanmoy Deb Vishnu Naresh Boddeti OOD 19 41 0 21 Dec 2022
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks Nikolaos Antoniou Efthymios Georgiou Alexandros Potamianos AAML 29 5 0 15 Dec 2022
Generative Robust Classification Xuwang Yin TPM 30 0 0 14 Dec 2022
DISCO: Adversarial Defense with Local Implicit Functions Chih-Hui Ho Nuno Vasconcelos AAML 21 38 0 11 Dec 2022
$Multiple Perturbation Attack: Attack Pixelwise Under Different $\ell_p$-norms For Better Adversarial Performance$ Multiple Perturbation Attack: Attack Pixelwise Under Different $\ell_p$ -norms For Better Adversarial Performance Ngoc N. Tran Anh Tuan Bui Dinh Q. Phung Trung Le AAML 21 1 0 05 Dec 2022
Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks Xiao-Li Li Ziqi Wang Bo-Wen Zhang Gang Hua Xiaolin Hu 29 25 0 04 Dec 2022
Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization Zifa Wang Nan Ding Tomer Levinboim Xi Chen Radu Soricut AAML 35 5 0 22 Nov 2022
MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing Attack Yunrui Yu Xitong Gao Chengzhong Xu AAML 28 8 0 15 Nov 2022
Efficient and Effective Augmentation Strategy for Adversarial Training Sravanti Addepalli Samyak Jain R. Venkatesh Babu AAML 62 58 0 27 Oct 2022
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games Maria-Florina Balcan Rattana Pukdee Pradeep Ravikumar Hongyang R. Zhang AAML 31 12 0 23 Oct 2022
Learning Sample Reweighting for Accuracy and Adversarial Robustness Chester Holtz Tsui-Wei Weng Gal Mishne OOD 28 4 0 20 Oct 2022
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining Hao Liu Tom Zahavy Volodymyr Mnih Satinder Singh SSL 38 7 0 19 Oct 2022
Effective Targeted Attacks for Adversarial Self-Supervised Learning Minseon Kim Hyeonjeong Ha Sooel Son Sung Ju Hwang AAML 39 3 0 19 Oct 2022
Improving Adversarial Robustness by Contrastive Guided Diffusion Process Yidong Ouyang Liyan Xie Guang Cheng 30 6 0 18 Oct 2022