A PCA-Based Model to Predict Adversarial Examples on Q-Learning of Path Finding

Xiang, Yingxiao; Niu, Wenjia; Liu, Jiqiang; Chen, Tong; Han, Zhen

doi:10.1109/dsc.2018.00125

Cited by 20 publications

(11 citation statements)

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“…Their experiments prove success of adversaries even in black-box scenarios. Xiang et al [216] developed a PCA-based model for predicting adversarial examples in the context of Q-learning based path-finding. In another related work, Bai et al [217] also attacked the Deep Q Network (DQN) [218] for robotic path-finding in a white-box setup.…”

Section: B Reinforcement Learningmentioning

confidence: 99%

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

2018

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Deep learning is at the heart of the current rise of artificial intelligence. In the field of Computer Vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas deep neural networks have demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has recently lead to a large influx of contributions in this direction. This article presents the first comprehensive survey on adversarial attacks on deep learning in Computer Vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them. To emphasize that adversarial attacks are possible in practical conditions, we separately review the contributions that evaluate adversarial attacks in the real-world scenarios. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.

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Section: B Reinforcement Learningmentioning

confidence: 99%

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

2018

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“…Other studies of adversarial attacks on the specific application of DRL for path-finding have also been conducted by (Xiang et al 2018) and (Bai et al 2018), which results in the RL agent failing to find a path to the goal or planning a path that is more costly.…”

Section: Adversarial Attacks On Rl Agentmentioning

confidence: 99%

Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents

Lee

Ghadai

Tan

et al. 2020

AAAI

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Robustness of Deep Reinforcement Learning (DRL) algorithms towards adversarial attacks in real world applications such as those deployed in cyber-physical systems (CPS) are of increasing concern. Numerous studies have investigated the mechanisms of attacks on the RL agent's state space. Nonetheless, attacks on the RL agent's action space (corresponding to actuators in engineering systems) are equally perverse, but such attacks are relatively less studied in the ML literature. In this work, we first frame the problem as an optimization problem of minimizing the cumulative reward of an RL agent with decoupled constraints as the budget of attack. We propose the white-box Myopic Action Space (MAS) attack algorithm that distributes the attacks across the action space dimensions. Next, we reformulate the optimization problem above with the same objective function, but with a temporally coupled constraint on the attack budget to take into account the approximated dynamics of the agent. This leads to the white-box Look-ahead Action Space (LAS) attack algorithm that distributes the attacks across the action and temporal dimensions. Our results showed that using the same amount of resources, the LAS attack deteriorates the agent's performance significantly more than the MAS attack. This reveals the possibility that with limited resource, an adversary can utilize the agent's dynamics to malevolently craft attacks that causes the agent to fail. Additionally, we leverage these attack strategies as a possible tool to gain insights on the potential vulnerabilities of DRL agents.

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“…FGSM (Goodfellow et al 2014a), SPA (Xiang et al 2018), WBA (Bai et al 2018), and CDG (Chen et al 2018b) belong to White-box attack, which have access to the details related to training algorithm and corresponding parameters of the target model. Meanwhile, the PIA (Behzadan and Munir 2017), STA (Lin et al 2017), EA (Lin et al 2017), and AVI (Liu et al 2017) are Black-box attacks, in which adversary has no idea of the details related to training algorithm and corresponding parameters of the model, for the threat model discussed in these literatures, authors assumed that the adversary has access to the training environment bat has no idea of the random initializations of the target policy, and additionally does not know what the learning algorithm is.…”

Section: Summary For Adversarial Attack In Reinforcement Learningmentioning

confidence: 99%

“…For instance, in the field of Atari game, Lin et al (2017) proposed a "strategicallytimed attack" whose adversarial example at each time step is computed independently of the adversarial examples at other time steps, instead of attacking a deep RL agent at every time step (see "Black-box attack" section). Moreover, in the terms of automatic path planning, Liu et al (2017), Xiang et al (2018), Bai et al (2018) and Chen et al (2018b) all proposed methods which can take adversarial attack on reinforcement learning algorithms (VIN (Tamar et al 2016), Q-Learning (Watkins and Dayan 1992), DQN (Mnih et al 2013), A3C (Mnih et al 2016)) under automatic path planning tasks (see "Defense technology against adversarial attack" section).…”

Section: Introductionmentioning

confidence: 99%

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Adversarial attack and defense in reinforcement learning-from AI security view

et al. 2019

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Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV). Therefore, a reliable RL system is the foundation for the security critical applications in AI, which has attracted a concern that is more critical than ever. However, recent studies discover that the interesting attack mode adversarial attack also be effective when targeting neural network policies in the context of reinforcement learning, which has inspired innovative researches in this direction. Hence, in this paper, we give the very first attempt to conduct a comprehensive survey on adversarial attacks in reinforcement learning under AI security. Moreover, we give briefly introduction on the most representative defense technologies against existing adversarial attacks.

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A PCA-Based Model to Predict Adversarial Examples on Q-Learning of Path Finding

Cited by 20 publications

References 11 publications

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents

Adversarial attack and defense in reinforcement learning-from AI security view

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