FURI | Spring 2019
The Reinforcement Learning Trojan Horse: Data Poisoning in Autonomous Driving Simulations
The objective of this research is to identify the presence of a specific, but potentially catastrophic, mathematical characteristic within a key machine learning aspect of the control system of autonomous vehicles. The conclusions of the study point to the presence of a mechanism in which a malicious adversary could include a seemingly undetectable backdoor into the controller of the autonomous car, enabling them to hack it at a strategic time. The identification of this threat enables autonomous car makers to hold off on the deployment of their fleet until a solution is identified. Future work entails optimizing the mechanism for injecting the backdoor, as well as developing a solution.
Hometown: Cupertino, California, United States
Graduation date: Spring 2021