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Found 7 papers, 3 papers with code
MIR2: Towards Provably Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization
Existing max-min optimization techniques in robust MARL seek to enhance resilience by training agents against worst-case adversaries, but this becomes intractable as the number of agents grows, leading to exponentially increasing worst-case scenarios.
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks
First, to benchmark attack naturalness, we contribute the first Physical Attack Naturalness (PAN) dataset with human rating and gaze.
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
To achieve maximum deviation in victim policies under complex agent-wise interactions, our unilateral attack aims to characterize and maximize the impact of the adversary on the victims.
Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection
The threat of fingerprint leakage from social media raises a strong desire for anonymizing shared images while maintaining image qualities, since fingerprints act as a lifelong individual biometric password.
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning
To overcome the challenge, we propose MARLSafe, the first robustness testing framework for c-MARL algorithms.
RNAS: Robust Network Architecture Search beyond DARTS
The vulnerability of Deep Neural Networks (DNNs) (i. e., susceptibility to adversarial attacks) severely limits the application of DNNs.
SpikeMS: Deep Spiking Neural Network for Motion Segmentation
Spiking Neural Networks (SNN) are the so-called third generation of neural networks which attempt to more closely match the functioning of the biological brain.
