A Deep Reinforcement Learning Approach for Adaptive GPS Spoofing against Multi-Sensor Fusion Localization System

Recent studies have revealed that Multi-Sensor Fusion (MSF) algorithms used in autonomous driving localization systems are still vulnerable to sensor spoofing attacks. However, the attack strategies explored in current research have largely remained unchanged, typically involving two fixed stages, as demonstrated in FusionRipper[1]. To address this limitation and investigate new strategies as well as higher attack thresholds, this paper presents an innovative sensor spoofing attack method that utilizes deep reinforcement learning, specifically targeting the widely used Error State Kalman Filter-based MSF localization algorithm. This technique allows for persistent spoofing attacks along a trajectory by training an agent to modify GPS sensor data inputs. In a scenario where the vehicle travels at a constant speed in a straight line, we demonstrate the effectiveness of this approach, achieving a higher attack offset upper limit than the current state-of-the-art methods. This new perspective on injecting false sensor data into the fusion algorithm not only establishes a higher attack threshold but also poses a significant threat to the security of autonomous driving systems.