Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

3 March 2020

Matthias Hein

Papers citing "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"

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Title
Phase-shifted Adversarial Training Yeachan Kim Seongyeon Kim Ihyeok Seo Bonggun Shin AAML OOD 24 0 0 12 Jan 2023
Beckman Defense A. V. Subramanyam OOD AAML 40 0 0 04 Jan 2023
Guidance Through Surrogate: Towards a Generic Diagnostic Attack Muzammal Naseer Salman Khan Fatih Porikli Fahad Shahbaz Khan AAML 28 1 0 30 Dec 2022
On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices Salah Ghamizi Maxime Cordy Michail Papadakis Yves Le Traon OOD 11 2 0 15 Dec 2022
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks Nikolaos Antoniou Efthymios Georgiou Alexandros Potamianos AAML 29 5 0 15 Dec 2022
Understanding Zero-Shot Adversarial Robustness for Large-Scale Models Chengzhi Mao Scott Geng Junfeng Yang Xin Eric Wang Carl Vondrick VLM 44 59 0 14 Dec 2022
SAIF: Sparse Adversarial and Imperceptible Attack Framework Tooba Imtiaz Morgan Kohler Jared Miller Zifeng Wang Octavia Camps Mario Sznaier Octavia Camps Jennifer Dy AAML 34 0 0 14 Dec 2022
Adversarially Robust Video Perception by Seeing Motion Lingyu Zhang Chengzhi Mao Junfeng Yang Carl Vondrick VGen AAML 44 2 0 13 Dec 2022
Robust Perception through Equivariance Chengzhi Mao Lingyu Zhang Abhishek Joshi Junfeng Yang Hongya Wang Carl Vondrick BDL AAML 29 7 0 12 Dec 2022
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments Xiaogang Xu Hengshuang Zhao Philip Torr Jiaya Jia AAML 29 2 0 11 Dec 2022
Re-purposing Perceptual Hashing based Client Side Scanning for Physical Surveillance Ashish Hooda Andrey Labunets Tadayoshi Kohno Earlence Fernandes 19 2 0 08 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 29 1 0 05 Dec 2022
Efficient Adversarial Input Generation via Neural Net Patching Tooba Khan Kumar Madhukar Subodh Vishnu Sharma AAML 24 0 0 30 Nov 2022
Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning Xiaoyue Duan Guoliang Kang Runqi Wang Shumin Han Shenjun Xue Tian Wang Baochang Zhang 29 2 0 28 Nov 2022
Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning Ethan Rathbun Kaleel Mahmood Sohaib Ahmad Caiwen Ding Marten van Dijk AAML 19 4 0 26 Nov 2022
Supervised Contrastive Prototype Learning: Augmentation Free Robust Neural Network Iordanis Fostiropoulos Laurent Itti 34 1 0 26 Nov 2022
Benchmarking Adversarially Robust Quantum Machine Learning at Scale Maxwell T. West S. Erfani C. Leckie M. Sevior Lloyd C. L. Hollenberg Muhammad Usman AAML OOD 27 33 0 23 Nov 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
Towards Robust Dataset Learning Yihan Wu Xinda Li Florian Kerschbaum Heng Huang Hongyang R. Zhang DD OOD 49 10 0 19 Nov 2022
Accelerating Adversarial Perturbation by 50% with Semi-backward Propagation Zhiqi Bu AAML 25 0 0 09 Nov 2022
Are AlphaZero-like Agents Robust to Adversarial Perturbations? Li-Cheng Lan Huan Zhang Ti-Rong Wu Meng-Yu Tsai I-Chen Wu Cho-Jui Hsieh AAML 21 10 0 07 Nov 2022
Data-free Defense of Black Box Models Against Adversarial Attacks Gaurav Kumar Nayak Inder Khatri Ruchit Rawal Anirban Chakraborty AAML 25 1 0 03 Nov 2022
ARDIR: Improving Robustness using Knowledge Distillation of Internal Representation Tomokatsu Takahashi Masanori Yamada Yuuki Yamanaka Tomoya Yamashita 20 0 0 01 Nov 2022
Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting Peng-Fei Hou Jie Han Xingyu Li AAML OOD 23 11 0 26 Oct 2022
Adversarial Purification with the Manifold Hypothesis Zhaoyuan Yang Zhiwei Xu Jing Zhang Richard I. Hartley Peter Tu AAML 24 5 0 26 Oct 2022
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network Hua Hua Jun Yan Xi Fang Weiquan Huang Huilin Yin Wancheng Ge AAML 25 1 0 25 Oct 2022
Learning Sample Reweighting for Accuracy and Adversarial Robustness Chester Holtz Tsui-Wei Weng Gal Mishne OOD 28 4 0 20 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
On the Adversarial Robustness of Mixture of Experts J. Puigcerver Rodolphe Jenatton C. Riquelme Pranjal Awasthi Srinadh Bhojanapalli OOD AAML MoE 42 18 0 19 Oct 2022
Scaling Adversarial Training to Large Perturbation Bounds Sravanti Addepalli Samyak Jain Gaurang Sriramanan R. Venkatesh Babu AAML 33 22 0 18 Oct 2022
DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers Gaurav Kumar Nayak Ruchit Rawal Anirban Chakraborty 11 3 0 17 Oct 2022
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture Yi Mo Dongxian Wu Yifei Wang Yiwen Guo Yisen Wang ViT 45 52 0 14 Oct 2022
Token-Label Alignment for Vision Transformers Han Xiao Wenzhao Zheng Zhengbiao Zhu Jie Zhou Jiwen Lu 21 4 0 12 Oct 2022
Visual Prompting for Adversarial Robustness Aochuan Chen P. Lorenz Yuguang Yao Pin-Yu Chen Sijia Liu VLM VPVLM 38 32 0 12 Oct 2022
Robust Models are less Over-Confident Julia Grabinski Paul Gavrikov J. Keuper M. Keuper AAML 36 24 0 12 Oct 2022
Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization Ziquan Liu Antoni B. Chan AAML 30 5 0 11 Oct 2022
Certified Training: Small Boxes are All You Need Mark Niklas Muller Franziska Eckert Marc Fischer Martin Vechev AAML 39 45 0 10 Oct 2022
A2: Efficient Automated Attacker for Boosting Adversarial Training Zhuoer Xu Guanghui Zhu Changhua Meng Shiwen Cui ZhenZhe Ying Weiqiang Wang GU Ming Yihua Huang AAML 36 13 0 07 Oct 2022
Game-Theoretic Understanding of Misclassification Kosuke Sumiyasu K. Kawamoto Hiroshi Kera 40 1 0 07 Oct 2022
Towards Out-of-Distribution Adversarial Robustness Adam Ibrahim Charles Guille-Escuret Ioannis Mitliagkas Irina Rish David M. Krueger P. Bashivan OOD 31 6 0 06 Oct 2022
Strength-Adaptive Adversarial Training Chaojian Yu Dawei Zhou Li Shen Jun Yu Bo Han Biwei Huang Nannan Wang Tongliang Liu OOD 17 2 0 04 Oct 2022
Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection Yiming Li Yang Bai Yong Jiang Yong-Liang Yang Shutao Xia Bo Li AAML 50 98 0 27 Sep 2022
Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training Saehyung Lee Hyungyu Lee AAML 29 2 0 27 Sep 2022
A Light Recipe to Train Robust Vision Transformers Edoardo Debenedetti Vikash Sehwag Prateek Mittal ViT 32 68 0 15 Sep 2022
Improving Robust Fairness via Balance Adversarial Training Chunyu Sun Chenye Xu Chengyuan Yao Siyuan Liang Yichao Wu Ding Liang XiangLong Liu Aishan Liu 23 11 0 15 Sep 2022
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization) Zhenyu Zhu Fanghui Liu Grigorios G. Chrysos V. Cevher 39 19 0 15 Sep 2022
On the interplay of adversarial robustness and architecture components: patches, convolution and attention Francesco Croce Matthias Hein 41 6 0 14 Sep 2022
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples Nuo Xu Kaleel Mahmood Haowen Fang Ethan Rathbun Caiwen Ding Wujie Wen AAML 29 12 0 07 Sep 2022
On the Transferability of Adversarial Examples between Encrypted Models Miki Tanaka Isao Echizen Hitoshi Kiya SILM 34 4 0 07 Sep 2022
Bag of Tricks for FGSM Adversarial Training Zichao Li Li Liu Zeyu Wang Yuyin Zhou Cihang Xie AAML 33 6 0 06 Sep 2022