Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning
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The danger of adversarial attacks to unprotected Uncrewed Aerial Vehicle (UAV) agents operating in public is growing. Adopting AI-based techniques and more specifically Deep Learning (DL) approaches to control and guide these UAVs can be beneficial in terms of performance but add more concerns regarding the safety of those techniques and their vulnerability against adversarial attacks causing the chances of collisions going up as the agent becomes confused. This paper proposes an innovative approach based on the explainability of DL methods to build an efficient detector that will protect these DL schemes and thus the UAVs adopting them from potential attacks. The agent is adopting a Deep Reinforcement Learning (DRL) scheme for guidance and planning. It is formed and trained with a Deep Deterministic Policy Gradient (DDPG) with Prioritised Experience Replay (PER) DRL scheme that utilises Artificial Potential Field (APF) to improve training times and obstacle avoidance performance. The adversarial attacks are generated by Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) algorithms and reduced obstacle course completion rates from 80% to 35%. A Realistic Synthetic environment for UAV explainable DRL based planning and guidance including obstacles and adversarial attacks is built. Two adversarial attack detectors are proposed. The first one adopts a Convolutional Neural Network (CNN) architecture and achieves an accuracy in detection of 80%. The second detector is developed based on a Long Short Term Memory (LSTM) network and achieves an accuracy of 91% with much faster computing times when compared to the CNN based detector.
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