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About Snooping Attacks On Deep Reinforcement Learning: Preprint. Under Review
The document discusses snooping attacks on deep reinforcement learning agents. It defines a new class of threat models called "snooping threat models" where the adversary can only eavesdrop on action and reward signals between the agent and environment, not interact directly. The document shows that even under these highly constrained models, adversaries can still launch effective attacks by training proxy models on related tasks and leveraging transferability of adversarial examples. Specifically, the document trains proxy models on Atari games to craft inputs that significantly reduce performance of agents trained on the same games, demonstrating the practical threat of snooping attacks on real-world systems like autonomous vehicles.
Detailed Information
| Author: | ['NoDa Mehrem'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 13 |
| Language: | English |
| Format: | |
| Price: | FREE |
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