advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch

20 February 2019

Papers citing "advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch"

40 / 40 papers shown

Title
Rethinking Target Label Conditioning in Adversarial Attacks: A 2D Tensor-Guided Generative Approach Hangyu Liu Bo Peng Pengxiang Ding Donglin Wang AAML 28 0 0 19 Apr 2025
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial Robustness Emanuele Ballarin A. Ansuini Luca Bortolussi AAML 62 0 0 20 Feb 2025
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Luke E. Richards Jessie Yaros Jasen Babcock Coung Ly Robin Cosbey Timothy Doster Cynthia Matuszek NAI 66 0 0 13 Feb 2025
Dual-Flow: Transferable Multi-Target, Instance-Agnostic Attacks via In-the-wild Cascading Flow Optimization Yixiao Chen Shikun Sun Jianshu Li Ruoyu Li Zhe Li Junliang Xing AAML 106 0 0 04 Feb 2025
Dormant: Defending against Pose-driven Human Image Animation Jiachen Zhou Mingsi Wang Tianlin Li Guozhu Meng Kai Chen 49 3 0 22 Sep 2024
Improving Adversarial Robustness via Decoupled Visual Representation Masking Decheng Liu Tao Chen Chunlei Peng Nannan Wang Ruimin Hu Xinbo Gao AAML 42 1 0 16 Jun 2024
One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models Hao Fang Jiawei Kong Wenbo Yu Bin Chen Jiawei Li Hao Wu Ke Xu Ke Xu AAML VLM 40 13 0 08 Jun 2024
On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks Nicholas H. Barbara Ruigang Wang I. Manchester 35 4 0 19 May 2024
Single-Class Target-Specific Attack against Interpretable Deep Learning Systems Eldor Abdukhamidov Mohammed Abuhamad George K. Thiruvathukal Hyoungshick Kim Tamer Abuhmed AAML 25 2 0 12 Jul 2023
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective Baoyuan Wu Zihao Zhu Li Liu Qingshan Liu Zhaofeng He Siwei Lyu AAML 44 21 0 19 Feb 2023
Accelerating Adversarial Perturbation by 50% with Semi-backward Propagation Zhiqi Bu AAML 25 0 0 09 Nov 2022
Towards Out-of-Distribution Adversarial Robustness Adam Ibrahim Charles Guille-Escuret Ioannis Mitliagkas Irina Rish David M. Krueger P. Bashivan OOD 29 6 0 06 Oct 2022
Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks Jiawei Zhang Jinwei Wang Hao Wang X. Luo AAML 25 28 0 26 Apr 2022
On the Effectiveness of Adversarial Training against Backdoor Attacks Yinghua Gao Dongxian Wu Jingfeng Zhang Guanhao Gan Shutao Xia Gang Niu Masashi Sugiyama AAML 32 22 0 22 Feb 2022
Exploring Adversarially Robust Training for Unsupervised Domain Adaptation Shao-Yuan Lo Vishal M. Patel AAML 18 8 0 18 Feb 2022
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness? P. Lorenz Dominik Strassel M. Keuper J. Keuper AAML 17 10 0 02 Dec 2021
Detecting AutoAttack Perturbations in the Frequency Domain P. Lorenz P. Harder Dominik Strassel M. Keuper J. Keuper AAML 9 13 0 16 Nov 2021
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks Maksym Yatsura J. H. Metzen Matthias Hein OOD 26 14 0 02 Nov 2021
Discriminator-Free Generative Adversarial Attack Shaohao Lu Yuqiao Xian Ke Yan Yi Hu Xing Sun Xiaowei Guo Feiyue Huang Weishi Zheng AAML GAN 33 20 0 20 Jul 2021
PAC Prediction Sets Under Covariate Shift Sangdon Park Edgar Dobriban Insup Lee Osbert Bastani 27 42 0 17 Jun 2021
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions Jing Wu Mingyi Zhou Ce Zhu Yipeng Liu Mehrtash Harandi Li Li AAML 44 10 0 22 Apr 2021
Removing Adversarial Noise in Class Activation Feature Space Dawei Zhou N. Wang Chunlei Peng Xinbo Gao Xiaoyu Wang Jun Yu Tongliang Liu AAML 25 28 0 19 Apr 2021
TOP: Backdoor Detection in Neural Networks via Transferability of Perturbation Todd P. Huster E. Ekwedike SILM 13 19 0 18 Mar 2021
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints Maura Pintor Fabio Roli Wieland Brendel Battista Biggio AAML 43 70 0 25 Feb 2021
Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data F. Cartella Orlando Anunciação Yuki Funabiki D. Yamaguchi Toru Akishita Olivier Elshocht AAML 61 71 0 20 Jan 2021
Composite Adversarial Attacks Xiaofeng Mao YueFeng Chen Shuhui Wang Hang Su Yuan He Hui Xue AAML 25 47 0 10 Dec 2020
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks Hao Shen Sihong Chen Ran Wang 24 5 0 27 Nov 2020
Incentives for Federated Learning: a Hypothesis Elicitation Approach Yang Liu Jiaheng Wei FedML 27 21 0 21 Jul 2020
Adversarial Example Games A. Bose Gauthier Gidel Hugo Berrard Andre Cianflone Pascal Vincent Simon Lacoste-Julien William L. Hamilton AAML GAN 33 51 0 01 Jul 2020
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks Linhai Ma Liang Liang AAML 18 18 0 19 May 2020
Encryption Inspired Adversarial Defense for Visual Classification Maungmaung Aprilpyone Hitoshi Kiya 16 32 0 16 May 2020
DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses Yaxin Li Wei Jin Han Xu Jiliang Tang AAML 19 129 0 13 May 2020
DaST: Data-free Substitute Training for Adversarial Attacks Mingyi Zhou Jing Wu Yipeng Liu Shuaicheng Liu Ce Zhu 6 142 0 28 Mar 2020
Toward Adversarial Robustness via Semi-supervised Robust Training Yiming Li Baoyuan Wu Yan Feng Yanbo Fan Yong Jiang Zhifeng Li Shutao Xia AAML 79 13 0 16 Mar 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 14 309 0 14 Feb 2020
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts Yu Sun Xiaolong Wang Zhuang Liu John Miller Alexei A. Efros Moritz Hardt TTA OOD 24 91 0 29 Sep 2019
Controlling Neural Level Sets Matan Atzmon Niv Haim Lior Yariv Ofer Israelov Haggai Maron Y. Lipman AI4CE 17 118 0 28 May 2019
MMA Training: Direct Input Space Margin Maximization through Adversarial Training G. Ding Yash Sharma Kry Yik-Chau Lui Ruitong Huang AAML 16 270 0 06 Dec 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 228 1,835 0 03 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 2016