Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns

• URL: http://arxiv.org/abs/2109.07723v1
• Date: Thu, 16 Sep 2021 04:59:06 GMT
• Title: Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns
• Authors: Prasanth Buddareddygari, Travis Zhang, Yezhou Yang, Yi Ren
• Abstract summary: Recent studies demonstrated the vulnerability of control policies learned through deep reinforcement learning against adversarial attacks. This paper investigates the feasibility of targeted attacks through visually learned patterns placed on physical object in the environment.
• Score: 18.694795507945603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies demonstrated the vulnerability of control policies learned through deep reinforcement learning against adversarial attacks, raising concerns about the application of such models to risk-sensitive tasks such as autonomous driving. Threat models for these demonstrations are limited to (1) targeted attacks through real-time manipulation of the agent's observation, and (2) untargeted attacks through manipulation of the physical environment. The former assumes full access to the agent's states/observations at all times, while the latter has no control over attack outcomes. This paper investigates the feasibility of targeted attacks through visually learned patterns placed on physical object in the environment, a threat model that combines the practicality and effectiveness of the existing ones. Through analysis, we demonstrate that a pre-trained policy can be hijacked within a time window, e.g., performing an unintended self-parking, when an adversarial object is present. To enable the attack, we adopt an assumption that the dynamics of both the environment and the agent can be learned by the attacker. Lastly, we empirically show the effectiveness of the proposed attack on different driving scenarios, perform a location robustness test, and study the tradeoff between the attack strength and its effectiveness.

Related papers

• Towards Transferable Attacks Against Vision-LLMs in Autonomous Driving with Typography [21.632703081999036]
  Vision-Large-Language-Models (Vision-LLMs) are increasingly being integrated into autonomous driving (AD) systems. We propose to leverage typographic attacks against AD systems relying on the decision-making capabilities of Vision-LLMs.
  arXiv  Detail & Related papers  (2024-05-23T04:52:02Z)
• On the Difficulty of Defending Contrastive Learning against Backdoor Attacks [58.824074124014224]
  We show how contrastive backdoor attacks operate through distinctive mechanisms. Our findings highlight the need for defenses tailored to the specificities of contrastive backdoor attacks.
  arXiv  Detail & Related papers  (2023-12-14T15:54:52Z)
• Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving [18.72382517467458]
  We propose a novel adversarial backdoor attack against trajectory prediction models. Our attack affects the victim at training time via naturalistic, hence stealthy, poisoned samples crafted using a novel two-step approach. We show that the proposed attack is highly effective, as it can significantly hinder the performance of prediction models.
  arXiv  Detail & Related papers  (2023-06-27T19:15:06Z)
• Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
  We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers. Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods. Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
  arXiv  Detail & Related papers  (2022-07-20T19:49:09Z)
• Deep-Attack over the Deep Reinforcement Learning [26.272161868927004]
  adversarial attack developments have made reinforcement learning more vulnerable. We propose a reinforcement learning-based attacking framework by considering the effectiveness and stealthy spontaneously. We also propose a new metric to evaluate the performance of the attack model in these two aspects.
  arXiv  Detail & Related papers  (2022-05-02T10:58:19Z)
• Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon [79.33449311057088]
  We study a new type of optical adversarial examples, in which the perturbations are generated by a very common natural phenomenon, shadow. We extensively evaluate the effectiveness of this new attack on both simulated and real-world environments.
  arXiv  Detail & Related papers  (2022-03-08T02:40:18Z)
• Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks [62.87459235819762]
  In a real-world scenario like autonomous driving, more attention should be devoted to real-world adversarial examples (RWAEs) This paper presents an in-depth evaluation of the robustness of popular SS models by testing the effects of both digital and real-world adversarial patches.
  arXiv  Detail & Related papers  (2021-08-13T11:49:09Z)
• Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles [21.894836150974093]
  In recent years, many deep learning models have been adopted in autonomous driving. Recent studies have demonstrated that adversarial attacks can cause a significant decline in detection precision of deep learning-based 3D object detection models.
  arXiv  Detail & Related papers  (2021-08-06T04:52:09Z)
• Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning [32.12283927682007]
  Deep reinforcement learning models are vulnerable to adversarial attacks which can decrease the victim's total reward by manipulating the observations. We reformulate the problem of adversarial attacks in function space and separate the previous gradient based attacks into several subspaces. In the first stage, we train a deceptive policy by hacking the environment, and discover a set of trajectories routing to the lowest reward. Our method provides a tighter theoretical upper bound for the attacked agent's performance than the existing approaches.
  arXiv  Detail & Related papers  (2021-06-30T07:41:51Z)
• Policy Smoothing for Provably Robust Reinforcement Learning [109.90239627115336]
  We study the provable robustness of reinforcement learning against norm-bounded adversarial perturbations of the inputs. We generate certificates that guarantee that the total reward obtained by the smoothed policy will not fall below a certain threshold under a norm-bounded adversarial of perturbation the input.
  arXiv  Detail & Related papers  (2021-06-21T21:42:08Z)
• Adversarial defense for automatic speaker verification by cascaded self-supervised learning models [101.42920161993455]
  More and more malicious attackers attempt to launch adversarial attacks at automatic speaker verification (ASV) systems. We propose a standard and attack-agnostic method based on cascaded self-supervised learning models to purify the adversarial perturbations. Experimental results demonstrate that the proposed method achieves effective defense performance and can successfully counter adversarial attacks.
  arXiv  Detail & Related papers  (2021-02-14T01:56:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.