Fetching the paper…
Reading the bibliography…
Recent research has confirmed the feasibility of backdoor attacks in deep reinforcement learning (RL) systems.
Stochastic games
Lloyd S Shapley · 1953
Earlier work this paper cites.
Lstm can solve hard long time lag problems
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
An automated fx trading system using adaptive reinforcement learning
Michael AH Dempster and Vasco Leemans · 2006
Earlier work this paper cites.
Safe, multi-agent, reinforcement learning for autonomous driving
Shai Shalev-Shwartz, Shaked Shammah, and Amnon Shashua · 2016
Earlier work this paper cites.
Emergent complexity via multi-agent competition
Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, and Igor Mordatch · 2017
Earlier work this paper cites.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
Earlier work this paper cites.
Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
Earlier work this paper cites.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
Earlier work this paper cites.
Adversarial attacks on neural network policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel · 2017
Earlier work this paper cites.
Tactics of adversarial attack on deep reinforcement learning agents
Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, and Min Sun · 2017
Earlier work this paper cites.
Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2017
Earlier work this paper cites.
Jpmorgan develops robot to execute trades
Laura Noonan · 2017
Cited alongside, same era.
Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2018
Cited alongside, same era.
Stable baselines
Ashley Hill, Antonin Raffin, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, Rene Traore, Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2018
Cited alongside, same era.
Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
Cited alongside, same era.
Clean-label backdoor attacks
Alexander Turner, Dimitris Tsipras, and Aleksander Madry · 2018
Cited alongside, same era.
Analyzing federated learning through an adversarial lens
Design of intentional backdoors in sequential models
Zhaoyuan Yang, Naresh Iyer, Johan Reimann, and Nurali Virani · 2019
Later among the works it cites.
Badnl: Backdoor attacks against nlp models
Xiaoyi Chen, Ahmed Salem, Michael Backes, Shiqing Ma, and Yang Zhang · 2020
Later among the works it cites.
Adversarial policies: Attacking deep reinforcement learning
Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, and Stuart Russell · 2020
Later among the works it cites.
Trojdrl: evaluation of backdoor attacks on deep reinforcement learning
Panagiota Kiourti, Kacper Wardega, Susmit Jha, and Wenchao Li · 2020
Later among the works it cites.
Hidden trigger backdoor attacks
Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2019
Cited alongside, same era.
Tabor: A highly accurate approach to inspecting and restoring trojan backdoors in ai systems
Wenbo Guo, Lun Wang, Xinyu Xing, Min Du, and Dawn Song · 2019
Cited alongside, same era.
Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan · 2019
Cited alongside, same era.
Label-consistent backdoor attacks
Alexander Turner, Dimitris Tsipras, and Aleksander Madry · 2019
Cited alongside, same era.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
Cited alongside, same era.
Dba: Distributed backdoor attacks against federated learning
Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li · 2019
Cited alongside, same era.
Ahmed Salem, Michael Backes, and Yang Zhang · 2020
Later among the works it cites.
Baaan: Backdoor attacks against autoencoder and gan-based machine learning models
Ahmed Salem, Yannick Sautter, Michael Backes, Mathias Humbert, and Yang Zhang · 2020
Later among the works it cites.
Gotta catch’em all: Using honeypots to catch adversarial attacks on neural networks
Shawn Shan, Emily Wenger, Bolun Wang, Bo Li, Haitao Zheng, and Ben Y Zhao · 2020
Later among the works it cites.
Attack of the tails: Yes, you really can backdoor federated learning
Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, and Dimitris Papailiopoulos · 2020
Later among the works it cites.
Yue Wang, Esha Sarkar, Michail Maniatakos, and Saif Eddin Jabari · 2020
Later among the works it cites.
Gangsweep: Sweep out neural backdoors by gan
Liuwan Zhu, Rui Ning, Cong Wang, Chunsheng Xin, and Hongyi Wu · 2020
Later among the works it cites.