Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2005.07099v1
• Date: Thu, 14 May 2020 16:06:38 GMT
• Title: Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning
• Authors: Jianwen Sun, Tianwei Zhang, Xiaofei Xie, Lei Ma, Yan Zheng, Kangjie Chen, Yang Liu
• Abstract summary: We introduce two novel adversarial attack techniques to emphstealthily and emphefficiently attack the Deep Reinforcement Learning agents. The first technique is the emphcritical 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 emphantagonist attack: the adversary automatically learns a domain-agnostic model to discover the critical moments of attacking the agent in an episode.
• Score: 30.46580767540506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A Novel Bifurcation Method for Observation Perturbation Attacks on Reinforcement Learning Agents: Load Altering Attacks on a Cyber Physical Power System [1.7887848708497243]
  This work proposes a novel attack technique for continuous control using Group Difference Logits loss with a bifurcation layer. We demonstrate the impacts of powerful gradient-based attacks in a realistic smart energy environment.
  arXiv  Detail & Related papers  (2024-07-06T20:55:24Z)
• Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks [62.036798488144306]
  Current defense mainly focuses on the known attacks, but the adversarial robustness to the unknown attacks is seriously overlooked. We propose an attack-agnostic defense method named Meta Invariance Defense (MID) We show that MID simultaneously achieves robustness to the imperceptible adversarial perturbations in high-level image classification and attack-suppression in low-level robust image regeneration.
  arXiv  Detail & Related papers  (2024-04-04T10:10:38Z)
• Guidance Through Surrogate: Towards a Generic Diagnostic Attack [101.36906370355435]
  We develop a guided mechanism to avoid local minima during attack optimization, leading to a novel attack dubbed Guided Projected Gradient Attack (G-PGA) Our modified attack does not require random restarts, large number of attack iterations or search for an optimal step-size. More than an effective attack, G-PGA can be used as a diagnostic tool to reveal elusive robustness due to gradient masking in adversarial defenses.
  arXiv  Detail & Related papers  (2022-12-30T18:45:23Z)
• The Space of Adversarial Strategies [6.295859509997257]
  Adversarial examples, inputs designed to induce worst-case behavior in machine learning models, have been extensively studied over the past decade. We propose a systematic approach to characterize worst-case (i.e., optimal) adversaries.
  arXiv  Detail & Related papers  (2022-09-09T20:53:11Z)
• Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning [6.414910263179327]
  We study reward poisoning attacks on online deep reinforcement learning (DRL) We demonstrate the intrinsic vulnerability of state-of-the-art DRL algorithms by designing a general, black-box reward poisoning framework called adversarial MDP attacks. Our results show that our attacks efficiently poison agents learning in several popular classical control and MuJoCo environments.
  arXiv  Detail & Related papers  (2022-05-30T04:07:19Z)
• LAS-AT: Adversarial Training with Learnable Attack Strategy [82.88724890186094]
  "Learnable attack strategy", dubbed LAS-AT, learns to automatically produce attack strategies to improve the model robustness. Our framework is composed of a target network that uses AEs for training to improve robustness and a strategy network that produces attack strategies to control the AE generation.
  arXiv  Detail & Related papers  (2022-03-13T10:21:26Z)
• Projective Ranking-based GNN Evasion Attacks [52.85890533994233]
  Graph neural networks (GNNs) offer promising learning methods for graph-related tasks. GNNs are at risk of adversarial attacks.
  arXiv  Detail & Related papers  (2022-02-25T21:52:09Z)
• Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems [50.758281304737444]
  We propose to explore adversarial examples and attack detection on reinforcement learning-based interactive recommendation systems. We first craft different types of adversarial examples by adding perturbations to the input and intervening on the casual factors. Then, we augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data.
  arXiv  Detail & Related papers  (2021-12-02T04:12:24Z)
• Composite Adversarial Attacks [57.293211764569996]
  Adversarial attack is a technique for deceiving Machine Learning (ML) models. In this paper, a new procedure called Composite Adrial Attack (CAA) is proposed for automatically searching the best combination of attack algorithms. CAA beats 10 top attackers on 11 diverse defenses with less elapsed time.
  arXiv  Detail & Related papers  (2020-12-10T03:21:16Z)
• Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques [13.972753012322126]
  Deep Neural Networks (DNNs) are well-known to be vulnerable to Adversarial Examples (AEs) Recently, advanced gradient-based attack techniques were proposed. In this paper, we make a steady step towards mitigating those advanced gradient-based attacks.
  arXiv  Detail & Related papers  (2020-05-27T23:42:25Z)

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.