Adversarial Machine Learning

Adversarial machine learning (AML) explores how to attack and defend against malicious manipulations of machine learning models. Current research focuses on developing novel attack strategies, particularly for real-time applications and diverse model types (e.g., image classifiers, language models, reinforcement learning agents), and designing robust defenses, including methods like adversarial training and purification models. The significance of AML research lies in its crucial role in securing increasingly prevalent machine learning systems across various sectors, from autonomous vehicles and 5G networks to malware detection and medical imaging, mitigating the risks posed by these vulnerabilities.

Papers

November 5, 2024

Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering
Fabrianne Effendi, Anupam Chattopadhyay
Encryption Method Adversarial Machine Learning Money Laundering

September 4, 2024

Adversarial Attacks on Machine Learning-Aided Visualizations
Takanori Fujiwara, Kostiantyn Kucher, Junpeng Wang, Rafael M. Martins, Andreas Kerren, Anders Ynnerman
Adversarial Attack New Machine Relevant Visualization Adversarial Machine Learning VIS4ML Research

August 31, 2024

LightPure: Realtime Adversarial Image Purification for Mobile Devices Using Diffusion Models
Hossein Khalili, Seongbin Park, Vincent Li, Brandan Bright, Ali Payani, Ramana Rao Kompella, Nader Sehatbakhsh
Diffusion Model Adversarial Training Adversarial Robustness Mobile Device Adversarial Purification Adversarial Machine Learning One Shot Generative

August 29, 2024

Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks
Yan Dai, Longbo Huang
Bandit Feedback Task Utility Online Linear Adversarial Machine Learning Stochastic Network

August 5, 2024

SEAS: Self-Evolving Adversarial Safety Optimization for Large Language Models
Muxi Diao, Rumei Li, Shiyang Liu, Guogang Liao, Jingang Wang, Xunliang Cai, Weiran Xu
Large Language Model Adversarial Attack Adversarial Prompt Upper Ocean Adversarial Machine Learning Adversarial Optimization

May 14, 2024

Adversarial Machine Learning Threats to Spacecraft
Rajiv Thummala, Shristi Sharma, Matteo Calabrese, Gregory Falco
Artificial Intelligence Adversarial Learning Unmanned Spacecraft Adversarial Machine Learning

November 22, 2023

Panda or not Panda? Understanding Adversarial Attacks with Interactive Visualization
Yuzhe You, Jarvis Tse, Jian Zhao
Adversarial Attack Adversarial Learning Interactive Visualization Adversarial Machine Learning Red PANDA

June 26, 2023

The race to robustness: exploiting fragile models for urban camouflage and the imperative for machine learning security
Harriet Farlow, Matthew Garratt, Gavin Mount, Tim Lynar
Adversarial Attack Native Robustness New Machine Adversarial Learning Object Detection Model Security Related Prey Camouflage Adversarial Machine Learning Model Collapse Adversarial Region

June 16, 2023

You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks
Edward Raff, Michel Benaroch, Andrew L. Farris
Machine Learning Native Robustness Adversarial Learning Adversarial Risk Adversarial Machine Learning

February 1, 2023

Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection
Aqib Rashid, Jose Such
Adversarial Attack Attack Strategy Adversarial Machine Learning Based Malware Detection Moving Target Defense

January 11, 2023

SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning
Maxwell Standen, Junae Kim, Claudia Szabo
Deep Reinforcement Learning Multi Agent Reinforcement Learning Multi Agent Learning Adversarial Machine Learning

December 29, 2022

"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice
Giovanni Apruzzese, Hyrum S. Anderson, Savino Dambra, David Freeman, Fabio Pierazzi, Kevin A. Roundy
Machine Learning Adversarial Learning Practice Mode Threat Model Gradient Computation Pseudo Victim AttackER Adversarial Machine Learning

December 28, 2022

Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks
Junlin Wu, Hussein Sibai, Yevgeniy Vorobeychik
Reinforcement Learning Adversarial Perturbation Partially Observable Markov Decision Process Adversarial Environment Adversarial Machine Learning Safety Certificate Supervised Reinforcement Learning

December 20, 2022

Learned Systems Security
Roei Schuster, Jin Peng Zhou, Thorsten Eisenhofer, Paul Grubbs, Nicolas Papernot
Adversarial Machine Learning Training Instance Attacker Model

September 25, 2022

Safety-compliant Generative Adversarial Networks for Human Trajectory Forecasting
Parth Kothari, Alexandre Alahi
Generative Adversarial GAN Training Human Trajectory Adversarial Machine Learning Multimodal Future Trajectory

July 21, 2022

Synthetic Dataset Generation for Adversarial Machine Learning Research
Xiruo Liu, Shibani Singh, Cory Cornelius, Colin Busho, Mike Tan, Anindya Paul, Jason Martin
Adversarial Example Synthetic Data Generation Synthetic Dataset Adversarial Machine Learning

July 4, 2022

Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples
Giovanni Apruzzese, Rodion Vladimirov, Aliya Tastemirova, Pavel Laskov
Machine Learning Adversarial Example G Network Adversarial Machine Learning Canary Exposure

May 20, 2022

SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning
Harsh Chaudhari, Matthew Jagielski, Alina Oprea
Diverse Ensemble Private Learning Model Ensemble Adversarial Machine Learning Secure Multiparty Computation Multi Party Computation

April 7, 2022

Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems
Furkan Mumcu, Keval Doshi, Yasin Yilmaz
Adversarial Attack Anomaly Detection Video Anomaly Detection Video Surveillance Adversarial Machine Learning

January 16, 2022

Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)
Edan Habler, Ron Bitton, Dan Avraham, Dudu Mimran, Eitan Klevansky, Oleg Brodt, Heiko Lehmann, Yuval Elovici, Asaf Shabtai
Radio Access Network Open Radio Access Network Adversarial Machine Learning Remediation Strategy

Adversarial Machine Learning

Papers

Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering

Adversarial Attacks on Machine Learning-Aided Visualizations

LightPure: Realtime Adversarial Image Purification for Mobile Devices Using Diffusion Models

Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks

SEAS: Self-Evolving Adversarial Safety Optimization for Large Language Models

Adversarial Machine Learning Threats to Spacecraft

Panda or not Panda? Understanding Adversarial Attacks with Interactive Visualization

The race to robustness: exploiting fragile models for urban camouflage and the imperative for machine learning security

You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks

Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection

SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning

"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice

Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks

Learned Systems Security

Safety-compliant Generative Adversarial Networks for Human Trajectory Forecasting

Synthetic Dataset Generation for Adversarial Machine Learning Research

Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples

SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning

Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems

Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)