Adversarial Attack

Adversarial attacks aim to deceive machine learning models by subtly altering input data, causing misclassifications or other erroneous outputs. Current research focuses on developing more robust models and detection methods, exploring various attack strategies across different model architectures (including vision transformers, recurrent neural networks, and graph neural networks) and data types (images, text, signals, and tabular data). Understanding and mitigating these attacks is crucial for ensuring the reliability and security of AI systems in diverse applications, from autonomous vehicles to medical diagnosis and cybersecurity.

Papers

February 19, 2023

February 18, 2023

StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
Yuqian Fu, Yu Xie, Yanwei Fu, Yu-Gang Jiang
LeArning Abstract Adversarial Attack Meta Learning Adversarial Learning Cross Domain

February 16, 2023

February 15, 2023

XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars
Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda
Adversarial Attack Adversarial Training Adversarial Robustness Crossbar Array Memristive Crossbar Robust Neural Architecture

February 14, 2023

On the Role of Randomization in Adversarially Robust Classification
Lucas Gnecco-Heredia, Yann Chevaleyre, Benjamin Negrevergne, Laurent Meunier, Muni Sreenivas Pydi
Adversarial Attack Adversarial Perturbation Integral Role Generative Adversarial Ensemble Classifier Adversarial Risk Randomization Program Deterministic Learner

February 12, 2023

TextDefense: Adversarial Text Detection based on Word Importance Entropy
Lujia Shen, Xuhong Zhang, Shouling Ji, Yuwen Pu, Chunpeng Ge, Xing Yang, Yanghe Feng
Adversarial Attack Adversarial Example Adversarial Text Textual Adversarial Attack Text Attack Lexicon Entropy

February 11, 2023

February 9, 2023

IB-RAR: Information Bottleneck as Regularizer for Adversarial Robustness
Xiaoyun Xu, Guilherme Perin, Stjepan Picek
Adversarial Attack Adversarial Example Adversarial Training Adversarial Robustness Information Bottleneck Gradient Regularization Adversarial Benchmark

February 8, 2023

February 7, 2023

Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
Simin Li, Jun Guo, Jingqiao Xiu, Pu Feng, Xin Yu, Aishan Liu, Wenjun Wu, Xianglong Liu
Adversarial Attack Cooperative Multi Agent Reinforcement Learning Multi Agent Interaction Multi Agent MuJoCo Adversarial Influence

February 4, 2023

CosPGD: a unified white-box adversarial attack for pixel-wise prediction tasks
Shashank Agnihotri, Steffen Jung, Margret Keuper
Adversarial Attack Semantic Adversarial White Box Adversarial Attack Pixel Wise Prediction Per Pixel Prediction

February 3, 2023

TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification
Lingfeng Shen, Ze Zhang, Haiyun Jiang, Ying Chen
Adversarial Attack Text Classification Adversarial Text Text Attack

February 2, 2023

February 1, 2023

Universal Soldier: Using Universal Adversarial Perturbations for Detecting Backdoor Attacks
Xiaoyun Xu, Oguzhan Ersoy, Stjepan Picek
Adversarial Attack Adversarial Example Backdoor Attack Backdoor Trigger Backdoor Detection Universal Robot