arXiv Machine Learning
Sep 22

Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision

The paper introduces RIBA, a reinforcement‑learning inspired black‑box adversarial attack that generates perturbations for neural networks with fewer queries than existing methods. RIBA achieves a 25.4% reduction in median queries on ResNet‑18/Cifar10 and a 22.5% reduction on Vit‑B/16/ImageNet, while matching white‑box attack performance on an adversarially trained model.

By Florian Krone, Elena Hoemann, Sven Hallerbach
arXiv Machine Learning
Sep 14

Robust Policy Optimization via Adversarial Importance Sampling

The paper introduces Adversarial Importance Sampling (Advis), a technique that leverages importance sampling over standard training trajectories to estimate and optimize worst‑case returns without extra environment interactions or auxiliary networks, thereby capturing long‑term robustness. It also presents advrl, a modular PyTorch library that consolidates existing robustness methods and adversarial attacks into single‑file implementations for easier prototyping and reproducible evaluation. Finally, the authors highlight that optimal adversarial hyperparameters do not transfer across agents, prompting evaluation against a broader set of attackers (6–14× more configurations) and demonstrate the effectiveness of their approach on continuous control tasks.

By Amine Andam, Jamal Bentahar, Mustapha Hedabou
arXiv AI
Sep 2

SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning

SEBA is a sample‑efficient framework for black‑box adversarial attacks on visual reinforcement learning agents. It combines a shadow Q model, a generative adversarial network for imperceptible perturbations, and a world model to simulate dynamics, reducing real‑world queries. Experiments on MuJoCo and Atari show SEBA significantly lowers cumulative rewards while preserving visual fidelity and requiring far fewer environment interactions than previous methods.

By Tairan Huang, Yulin Jin, Junxu Liu, Qingqing Ye, Haibo Hu