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Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning
Jianwen Sun, Tianwei Zhang, Xiaofei Xie, Lei Ma, Yan Zheng, Kangjie Chen, Yang Liu
TL;DR
Adversarial attacks on DNN-based DRL policies have received little attention, although understanding their possibility and cost matters for safety- and security-sensitive decision tasks. Critical Point Attack predicts near-future states and assesses possible strategies, while Antagonist Attack learns a domain-agnostic model to select attack moments using the agent’s end-goal reward. The attacks compromise Atari, TORCS, and Mojuco tasks; on TORCS, Critical Point Attack needs 1 step versus about 80 for strategically-timed attack.
Problem
Adversarial attacks on DNN-based DRL policies have received little attention, although understanding their possibility and cost matters for safety- and security-sensitive decision tasks.
Source: Introduction · p. 1Method
Critical Point Attack predicts near-future states and assesses possible strategies, while Antagonist Attack learns a domain-agnostic model to select attack moments using the agent’s end-goal reward.
Source: Introduction · p. 2Results
The attacks compromise Atari, TORCS, and Mojuco tasks; on TORCS, Critical Point Attack needs 1 step versus about 80 for strategically-timed attack.
Source: Summary · pp. 7–8Takeaways & Limitations
The authors conclude that their approaches can identify critical perturbation moments and inflict severe damage with fewer attack steps in evaluated environments.
Source: Conclusion · p. 8The problem formulation assumes the attack-step cap N is far smaller than episode length T.
Source: Problem Statement and Attack Overview · p. 3
Abstract
from arXiv · showhide
Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed. However, the possibility and feasibility of such attacks against Deep Reinforcement Learning (DRL) are less explored. As DRL has achieved great success in various complex tasks, designing effective adversarial attacks is an indispensable prerequisite towards building robust DRL algorithms. In this paper, we introduce two novel adversarial attack techniques to \emph{stealthily} and \emph{efficiently} attack the DRL agents. These two techniques enable an adversary to inject adversarial samples in a minimal set of critical moments while causing the most severe damage to the agent. The first technique is the \emph{critical point attack}: the adversary builds a model to predict the future environmental states and agent's actions, assesses the damage of each possible attack strategy, and selects the optimal one. The second technique is the \emph{antagonist attack}: the adversary automatically learns a domain-agnostic model to discover the critical moments of attacking the agent in an episode. Experimental results demonstrate the effectiveness of our techniques. Specifically, to successfully attack the DRL agent, our critical point technique only requires 1 (TORCS) or 2 (Atari Pong and Breakout) steps, and the antagonist technique needs fewer than 5 steps (4 Mujoco tasks), which are significant improvements over state-of-the-art methods.
Introduction
The introduction frames DRL attacks as a problem of harming task-level outcomes with few perturbation steps, then presents two attack strategies and evaluations across tasks and algorithms.
Introduction
- DRL attacks must compromise a sequence-dependent task goal while perturbing very few steps, since repeated attacks can be detected and corrected.Unlike simply misleading one prediction, the attack must affect the agent’s end goal.Source: Introduction · p. 1
- Earlier DRL attacks either targeted per-step mispredictions, ignored downstream effects, or struggled to predict and enforce future states and actions.The strategically-timed attack also could not control the number of attacked steps, while enchanting attack had a low success rate.Source: Introduction · p. 2
- Critical Point Attack predicts future states and damage from possible strategies, then uses an explicit damage metric to select critical perturbation moments.The prediction model and damage assessment support choosing a small number of attack steps.Source: Introduction · p. 2
- Antagonist Attack learns a domain-agnostic model that uses the agent’s end-goal reward to identify when and how to perturb.The contribution is described as discovering an optimal strategy with the least attack cost and without domain knowledge.Source: Introduction · p. 2
- Critical Point Attack needs 1 step on TORCS and 2 steps on Atari Pong and Breakout, versus 25% of total steps for Lin et al. (2017).Antagonist Attack can successfully attack Atari and Mujoco within 5 steps.Source: Introduction · p. 2
Related Work
Related work spans adversarial examples for supervised systems and attacks on DRL policies, but many DRL approaches target individual states or use substantial attack budgets rather than selecting task-critical moments.
Related Work
- Supervised-DNN adversarial examples were studied across image, speech, and language tasks; DRL attacks perturb state observations to alter actions and potentially task outcomes.Source: Related Work · p. 2
- DRL attack studies examined transferability, training- and test-time robustness, worst-action attacks, targeted policies, and black-box settings.Source: Related Work · p. 2
- Prior DRL attacks often targeted specific states while ignoring the end goal across a continuous sequence, making perturbations inefficient and easier to detect.The review identifies choosing optimal injection moments as a direction for reducing attack cost.Source: Related Work · pp. 2–3
- Attacking every N frames reduced injection frequency, but still required about 120 attacked states per episode to affect agent performance (Kos and Song 2017).Source: Related Work · p. 3
- Strategically-timed attack used a threshold on action preference but considered only the current step and worked only for A3C and DQN with discrete actions (Lin et al. 2017).Source: Related Work · p. 3
- Enchanting attack precomputed adversarial states to steer the agent toward a damaged state, but dynamic behavior was difficult to predict and control (Lin et al. 2017).The reviewed approaches still required 25% of total steps to succeed.Source: Related Work · p. 3
- The paper aims to reduce the number of attack steps while achieving similar or better results through two new techniques.Source: Related Work · p. 3
Adversarial Attacks
The section formulates stealthy DRL attack as minimizing the agent’s expected reward with a small attack-step budget, then separates moment selection from perturbation construction. It presents predictive Critical Point and learned Antagonist approaches.
Problem Statement and Attack Overview
- The adversary minimizes expected reward while limiting perturbations to at most N steps, far fewer than the episode length.The approach separates deciding when to attack from generating the corresponding perturbations.Source: Problem Statement and Attack Overview · p. 3
Critical Point Attack
- Critical Point Attack predicts future states and actions, evaluates N-step perturbation sequences at t+M by state divergence, and attacks when damage exceeds a threshold.Its divergence function must be domain-specific; reward-based prediction became inaccurate over longer prediction horizons.Source: Critical Point Attack · pp. 3–4
Antagonist Attack
- Antagonist Attack learns a domain-agnostic policy mapping states to whether to perturb and which incorrect action to induce, training against the victim’s reward.The policy supplies an attack probability and target action at each step.Source: Antagonist Attack · p. 5
Adding Perturbation
- After selecting critical moments and target actions, the attack uses C&W to craft perturbations that make the policy network choose the target action.The target action is the one selected at the critical moment.Source: Adding Perturbation · p. 5
Experiments
Across Atari, TORCS, and Mujoco, both attacks compromise DRL tasks with fewer attack steps than strategically-timed attacks in the reported comparisons. Critical Point Attack uses a domain-specific prediction model, while Antagonist Attack learns without domain knowledge.
Experiments
- The evaluation targets Atari Pong and Breakout, TORCS, and four Mujoco tasks, using A3C, DDPG, and PPO agents and averaging attack tests over 20 random seeds.The paper frames the experiments around attack effectiveness and efficiency across tasks and algorithms.Source: Experimental Settings and… · p. 6, Experiments · p. 6
Experimental Settings and Implementations
- For Atari, the prediction model is trained on game RAM because images do not directly expose metrics such as the ball’s position; TORCS sensory data directly trains its prediction model.The reported training and testing sets contain about 20M and 5M frames, respectively.Source: Experimental Settings and… · p. 6
Summary
- For similar effects, Atari Critical Point Attack needs 1 or 2 steps and Antagonist Attack 3, versus strategically-timed attack (Lin et al. 2017) requiring 5 or 6; in TORCS, counts are 1 versus about 80.Source: Summary · pp. 7–8
Results of Critical Point Attack
- For Pong and Breakout, Critical Point Attack reduces average return with a 2-step attack at Δ values of [9, 56] and [23, 43], respectively.Lower Δ may make attacks less effective; higher Δ makes suitable timing harder to find.Source: Results of Critical Point Attack · p. 6, Figure 2 · p. 6
- Critical Point Attack’s damage metric follows similar critical-step trends to strategically-timed attack but is described as more precise and explicit; in TORCS, it predicts critical moments with 81.5% accuracy.On a 2,000-episode TORCS evaluation, a 1-step attack is most effective at Δ about 0.82.Source: Results of Critical Point Attack · pp. 6–7
Results of Antagonist Attack
- Antagonist Attack breaks Atari agents in 3 steps and reduces rewards in Mujoco using 1–5 steps, with each Mujoco attack taking about 4 ms per step.The reported Mujoco attacks break the agent within 20 ms.Source: Results of Antagonist Attack · p. 7, Figure 4 · p. 8
Summary
- The evaluation reports attacks across Atari, TORCS, and Mujoco and across A3C, DDPG, and PPO, while strategically-timed attack is limited to specific algorithms and discrete-action tasks.The paper states the proposed techniques can be applied to other DRL scenarios and environments.Source: Summary · pp. 7–8
Conclusion
The paper concludes that its two attack techniques identify critical moments for perturbation, causing severe damage with fewer steps than past work.
Conclusion
- The two techniques identify critical moments for perturbation that incur the most severe damage to DRL agents.The paper evaluates them in different types of DRL environments.Source: Conclusion · p. 8
- The evaluated techniques need much fewer attack steps than past work to achieve the same effects.Source: Conclusion · p. 8
- Future work will train the antagonist policy with multiple safety goals and enhance DRL algorithm robustness.Source: Conclusion · p. 8