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

September 24, 2024

Proactive Schemes: A Survey of Adversarial Attacks for Social Good
Vishal Asnani, Xi Yin, Xiaoming Liu
Deep Learning Timely Survey Adversarial Attack Deep Learning Model Deep Learning Technology ProActive Behavior
Adversarial Watermarking for Face Recognition
Yuguang Yao, Anil Jain, Sijia Liu
Adversarial Attack Adversarial Perturbation Face Recognition Watermarking Method Agnostic Watermarking Adversarial Watermarking

September 23, 2024

Log-normal Mutations and their Use in Detecting Surreptitious Fake Images
Ismail Labiad, Thomas Bäck, Pierre Fernandez, Laurent Najman, Tom Sanders, Furong Ye, Mariia Zameshina, Olivier Teytaud
Adversarial Attack Greater Public Use Traditional Adversarial Sigma Lognormal
Improving Adversarial Robustness for 3D Point Cloud Recognition at Test-Time through Purified Self-Training
Jinpeng Lin, Xulei Yang, Tianrui Li, Xun Xu
Adversarial Attack Adversarial Training Adversarial Robustness Self Training Adversarial Sample Test Time Adversarial Purification Point Cloud Recognition
Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI
Ambrish Rawat, Stefan Schoepf, Giulio Zizzo, Giandomenico Cornacchia, Muhammad Zaid Hameed, Kieran Fraser, Erik Miehling, Beat Buesser, Elizabeth M. Daly, Mark Purcell, Prasanna Sattigeri, Pin-Yu Chen, Kush R. Varshney
Adversarial Attack Technical Challenge Generative AI Visual Perspective Common Pitfall Red Teaming Generative AI System Adversarial Risk Adversarial Threat

September 21, 2024

Cloud Adversarial Example Generation for Remote Sensing Image Classification
Fei Ma, Yuqiang Feng, Fan Zhang, Yongsheng Zhou
Adversarial Attack Adversarial Example Adversarial Perturbation Remote Sensing Image Classification Adversarial Cloud
Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image Generation
G M Shahariar, Jia Chen, Jiachen Li, Yue Dong
Adversarial Attack Empirical Study Speech Analysis Text to Image Generation Text to Image Gradient Based Attack Part Identification Po Tagger Adversarial Suffix

September 20, 2024

ViTGuard: Attention-aware Detection against Adversarial Examples for Vision Transformer
Shihua Sun, Kenechukwu Nwodo, Shridatt Sugrim, Angelos Stavrou, Haining Wang
Convolutional Neural Network Adversarial Attack Vision Transformer Adversarial Example Adversarial Patch Attack
Efficient Visualization of Neural Networks with Generative Models and Adversarial Perturbations
Athanasios Karagounis
Neural Network Adversarial Attack Generative Model Adversarial Example Adversarial Perturbation Generative Network RIN Visualization
Deterministic versus stochastic dynamical classifiers: opposing random adversarial attacks with noise
Lorenzo Chicchi, Duccio Fanelli, Diego Febbe, Lorenzo Buffoni, Francesca Di Patti, Lorenzo Giambagli, Raffele Marino
Adversarial Attack Industrial Disturbing Noise Dynamic Learning New Attractor Critical Neuron
Relationship between Uncertainty in DNNs and Adversarial Attacks
Abigail Adeniran, Adewale Adeyemo
Neural Network Deep Neural Network Adversarial Attack High Uncertainty Anticipation DNN Framework Model Uncertainty Human Relationship
Hidden Activations Are Not Enough: A General Approach to Neural Network Predictions
Samuel Leblanc, Aiky Rasolomanana, Marco Armenta
Neural Network Adversarial Attack Adversarial Example General Framework Hidden Knowledge MLP Architecture Neural Network Prediction Quiver Representation

September 19, 2024

Deep generative models as an adversarial attack strategy for tabular machine learning
Salijona Dyrmishi, Mihaela Cătălina Stoian, Eleonora Giunchiglia, Maxime Cordy
Adversarial Attack Generative Adversarial Network Adversarial Example Tabular Machine Learning
TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN
Ziyi Liu, Dengpan Ye, Long Tang, Yunming Zhang, Jiacheng Deng
Adversarial Attack Adversarial Example Recurrent Neural Network Network Intrusion Detection Adaptive Metric
ITPatch: An Invisible and Triggered Physical Adversarial Patch against Traffic Sign Recognition
Shuai Yuan, Hongwei Li, Xingshuo Han, Guowen Xu, Wenbo Jiang, Tao Ni, Qingchuan Zhao, Yuguang Fang
Adversarial Attack Adversarial Patch Traffic Sign Recognition Single Simple Patch Attack Vector Physical Adversarial Patch
Enhancing 3D Robotic Vision Robustness by Minimizing Adversarial Mutual Information through a Curriculum Training Approach
Nastaran Darabi, Dinithi Jayasuriya, Devashri Naik, Theja Tulabandhula, Amit Ranjan Trivedi
Adversarial Attack Adversarial Example Adversarial Robustness Adversarial Perturbation 3D Perception Adversarial Objective

September 18, 2024

A constrained optimization approach to improve robustness of neural networks
Shudian Zhao, Jan Kronqvist
Neural Network Adversarial Attack Native Robustness Fine Tuning Constrained Optimization Nonconvex Optimization Adversarial Data
NPAT Null-Space Projected Adversarial Training Towards Zero Deterioration
Hanyi Hu, Qiao Han, Kui Chen, Yao Yang
Adversarial Attack Adversarial Training High Performance Null Space Projected Gradient Descent

September 17, 2024

Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification
Deepsayan Sadhukhan, Nitin Priyadarshini Shankar, Sheetal Kalyani
Deep Learning Adversarial Attack Adversarial Training Attack Strategy Modulation Classification Automatic Modulation Classification
EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage
Zeyi Liao, Lingbo Mo, Chejian Xu, Mintong Kang, Jiawei Zhang, Chaowei Xiao, Yuan Tian, Bo Li, Huan Sun
Adversarial Attack PRIvacy Leakage Adversarial Environment Web Agent

Adversarial Attack

Papers

Proactive Schemes: A Survey of Adversarial Attacks for Social Good

Adversarial Watermarking for Face Recognition

Log-normal Mutations and their Use in Detecting Surreptitious Fake Images

Improving Adversarial Robustness for 3D Point Cloud Recognition at Test-Time through Purified Self-Training

Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI

Cloud Adversarial Example Generation for Remote Sensing Image Classification

Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image Generation

ViTGuard: Attention-aware Detection against Adversarial Examples for Vision Transformer

Efficient Visualization of Neural Networks with Generative Models and Adversarial Perturbations

Deterministic versus stochastic dynamical classifiers: opposing random adversarial attacks with noise

Relationship between Uncertainty in DNNs and Adversarial Attacks

Hidden Activations Are Not Enough: A General Approach to Neural Network Predictions

Deep generative models as an adversarial attack strategy for tabular machine learning

TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN

ITPatch: An Invisible and Triggered Physical Adversarial Patch against Traffic Sign Recognition

Enhancing 3D Robotic Vision Robustness by Minimizing Adversarial Mutual Information through a Curriculum Training Approach

A constrained optimization approach to improve robustness of neural networks

NPAT Null-Space Projected Adversarial Training Towards Zero Deterioration

Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification

EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage