4 citations · 6 across the 11 of their papers we have counts for
11 papers
Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning
Wo Wei Lin, Ethan Rathbun, Enrico Marchesini +1
Multi-agent reinforcement learning (MARL) in real-world use cases may need to adapt to external natural language instructions that interrupt ongoing behavior and conflict with long…
Targeting World Models to Compromise Robot Learning Pipelines
Ethan Rathbun, Ahmed Agha, Saaduddin Mahmud +3
World models have recently seen a rapid growth in both their popularity and capability as more data efficient tools for generating robot training data or simulating real world envi…
Beware Untrusted Simulators -- Reward-Free Backdoor Attacks in Reinforcement Learning
Ethan Rathbun, Wo Wei Lin, Alina Oprea +1
Simulated environments are a key piece in the success of Reinforcement Learning (RL), allowing practitioners and researchers to train decision making agents without running expensi…
Busting the Paper Ballot: Voting Meets Adversarial Machine Learning
Kaleel Mahmood, Caleb Manicke, Ethan Rathbun +5
We show the security risk associated with using machine learning classifiers in United States election tabulators. The central classification task in election tabulation is decidin…
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense
Aditya Vikram Singh, Ethan Rathbun, Emma Graham +4
Recent advances in multi-agent reinforcement learning (MARL) have created opportunities to solve complex real-world tasks. Cybersecurity is a notable application area, where defend…
Adversarial Inception Backdoor Attacks against Reinforcement Learning
Ethan Rathbun, Alina Oprea, Christopher Amato
Recent works have demonstrated the vulnerability of Deep Reinforcement Learning (DRL) algorithms against training-time, backdoor poisoning attacks. The objectives of these attacks…
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents
Ethan Rathbun, Christopher Amato, Alina Oprea
Reinforcement learning (RL) is an actively growing field that is seeing increased usage in real-world, safety-critical applications -- making it paramount to ensure the robustness…
Distilling Adversarial Robustness Using Heterogeneous Teachers
Jieren Deng, Aaron Palmer, Rigel Mahmood +4
Achieving resiliency against adversarial attacks is necessary prior to deploying neural network classifiers in domains where misclassification incurs substantial costs, e.g., self-…
Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning
Ethan Rathbun, Kaleel Mahmood, Sohaib Ahmad +2
Recent advances in adversarial machine learning have shown that defenses considered to be robust are actually susceptible to adversarial attacks which are specifically customized t…
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
Nuo Xu, Kaleel Mahmood, Haowen Fang +3
Spiking neural networks (SNNs) have attracted much attention for their high energy efficiency and recent advances in classification performance. However, unlike traditional deep le…
Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks
Kaleel Mahmood, Rigel Mahmood, Ethan Rathbun +1
The field of adversarial machine learning has experienced a near exponential growth in the amount of papers being produced since 2018. This massive information output has yet to be…