Towards Defending against Adversarial Examples via Attack-Invariant
Features

Towards Defending against Adversarial Examples via Attack-Invariant Features

9 June 2021

Bo Han

Xinbo Gao

Papers citing "Towards Defending against Adversarial Examples via Attack-Invariant Features"

9 / 9 papers shown

Title
Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain Gaozheng Pei Ke Ma Yingfei Sun Qianqian Xu Qingming Huang DiffM 45 0 0 02 May 2025
Improving Adversarial Robustness via Decoupled Visual Representation Masking Decheng Liu Tao Chen Chunlei Peng Nannan Wang Ruimin Hu Xinbo Gao AAML 51 1 0 16 Jun 2024
Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors Jiachen Sun Changsheng Wang Jiong Wang Yiwei Zhang Chaowei Xiao AAML VLM 39 3 0 17 May 2024
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation Yifan Wu Jiawei Du Ping Liu Yuewei Lin Wenqing Cheng Wei-ping Xu DD AAML 40 5 0 20 Mar 2024
Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSM Linyu Tang Lei Zhang AAML 35 3 0 18 Mar 2024
Improving Adversarial Robustness via Mutual Information Estimation Dawei Zhou Nannan Wang Xinbo Gao Bo Han Xiaoyu Wang Yibing Zhan Tongliang Liu AAML 19 15 0 25 Jul 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
Modeling Adversarial Noise for Adversarial Training Dawei Zhou Nannan Wang Bo Han Tongliang Liu AAML 38 15 0 21 Sep 2021
Removing Adversarial Noise in Class Activation Feature Space Dawei Zhou N. Wang Chunlei Peng Xinbo Gao Xiaoyu Wang Jun Yu Tongliang Liu AAML 30 28 0 19 Apr 2021