ACADIA: Efficient and Robust Adversarial Attacks Against Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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ACADIA: Efficient and Robust Adversarial Attacks Against Deep Reinforcement Learning

Published:2022-10-03 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Conference on Communications and Network Security (CNS)
language:
Short-container-title:

Author:

Ali Haider¹,Al Ameedi Mohannad¹,Swami Ananthram²,Ning Rui³,Li Jiang⁴,Wu Hongyi⁵,Cho Jin-Hee¹

Affiliation:

1. Computer Science, Virginia Tech,VA

2. Army Research Laboratory,MD,USA

3. Old Dominion University,Computer Science,VA,USA

4. Old Dominion University,Electrical Engineering,VA,USA

5. The University of Arizona,Electrical and Computer Engineering,AZ,USA

Funder

NSF

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9947203/9947223/09947234.pdf?arnumber=9947234

Reference26 articles.

1. Adversarial ex-amples in the physical world;kurakin;5th International Conference on Learning Representations ICLR 2017,2017

2. Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

3. Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

4. Playing Atari with deep reinforcement learning;mnih;ArXiv Preprint,2013

5. Human-level control through deep reinforce-ment learning;mnih;Nature,2015

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploring the Efficacy of Learning Techniques in Model Extraction Attacks on Image Classifiers: A Comparative Study;Applied Sciences;2024-04-29

2. Towards cost-effective and resource-aware aggregation at Edge for Federated Learning;2023 IEEE International Conference on Big Data (BigData);2023-12-15

3. A Survey on Attacks and Their Countermeasures in Deep Learning: Applications in Deep Neural Networks, Federated, Transfer, and Deep Reinforcement Learning;IEEE Access;2023

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