Recent studies have shown that deep reinforcement learning agents are vulnerable
to small adversarial perturbations on the agent’s inputs, which raises concerns
about deploying such agents in the real world. To address this issue, we propose
RADIAL-RL, a principled framework to train reinforcement learning agents with
improved robustness against lp-norm bounded adversarial attacks. Our framework
is compatible with popular deep reinforcement learning algorithms and we
demonstrate its performance with deep Q-learning, A3C and PPO. We experiment
on three deep RL benchmarks (Atari, MuJoCo and ProcGen) to show the
effectiveness of our robust training algorithm. Our RADIAL-RL agents consistently
outperform prior methods when tested against attacks of varying strength
and are more computationally efficient to train. In addition, we propose a new
evaluation method called Greedy Worst-Case Reward (GWC) to measure attack
agnostic robustness of deep RL agents. We show that GWC can be evaluated
efficiently and is a good estimate of the reward under the worst possible sequence
of adversarial attacks. All code used for our experiments is available
at https://github.com/tuomaso/radial_rl_v2.
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PATCH-FOOL: ARE VISION TRANSFORMERS ALWAYS ROBUST AGAINST ADVERSARIAL PERTURBATIONS?
Vision transformers (ViTs) have recently set off a new wave in neural architecture design thanks to their record-breaking performance in various vision tasks. In parallel, to fulfill the goal of deploying ViTs into real-world vision applications, their robustness against potential malicious attacks has gained increasing attention. In particular, recent works show that ViTs are more robust against adversarial attacks as compared with convolutional neural networks (CNNs), and conjecture that this is because ViTs focus more on capturing global interactions among different input/feature patches, leading to their improved robustness to local perturbations imposed by adversarial attacks. In this work, we ask an intriguing
question: “Under what kinds of perturbations do ViTs become more vulnerable learners compared to CNNs?” Driven by this question, we first conduct a comprehensive experiment regarding the robustness of both ViTs and CNNs under various existing adversarial attacks to understand the underlying reason favoring their robustness. Based on the drawn insights, we then propose a dedicated attack framework, dubbed Patch-Fool, that fools the self-attention mechanism by attacking its basic component (i.e., a single patch) with a series of attention-aware optimization techniques. Interestingly, our Patch-Fool framework shows for the first time that ViTs are not necessarily more robust than CNNs against adversarial perturbations. In particular, we find that ViTs are more vulnerable learners compared with CNNs against our Patch-Fool attack which is consistent across extensive experiments, and the observations from Sparse/Mild Patch-Fool, two variants of Patch-Fool, indicate an intriguing insight that the perturbation density
and strength on each patch seem to be the key factors that influence the robustness ranking between ViTs and CNNs. It can be expected that our Patch-Fool framework will shed light on both future architecture designs and training schemes for robustifying ViTs towards their real-world deployment. Our codes are available at https://github.com/RICE-EIC/Patch-Fool.
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- Award ID(s):
- 1937592
- NSF-PAR ID:
- 10358516
- Date Published:
- Journal Name:
- Tenth International Conference on Learning Representations (ICLR 2022)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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