@inproceedings{c4b3ae68bd224a76851a0c4ea3e7f23c,
title = "Ignore the noise: Using autoencoders against adversarial attacks in reinforcement learning (lightning talk)",
abstract = "Reinforcement learning (RL) algorithms learn and explore nearly any state any number of times in their environment, but minute adversarial attacks cripple these agents. In this work, we define our threat model against RL agents as such: Adversarial agents introduce small permutations to the input data via black-box models with the goal of reducing the optimality of the agent. We focus on pre-processing adversarial images before they enter the network to reconstruct the ground-truth images.",
keywords = "Adversarial examples, Autoencoders, Reinforcement learning",
author = "William Aiken and Hyoungshick Kim",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 4th International Conference on Software Security and Assurance, ICSSA 2018 ; Conference date: 26-07-2018 Through 27-07-2018",
year = "2018",
month = jul,
doi = "10.1109/ICSSA45270.2018.00028",
language = "English",
series = "Proceedings - 2018 4th International Conference on Software Security and Assurance, ICSSA 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "81",
booktitle = "Proceedings - 2018 4th International Conference on Software Security and Assurance, ICSSA 2018",
}