The dangers of adversarial attacks on Uncrewed Aerial Vehicle (UAV) agents
operating in public are increasing. 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 can add concerns regarding the safety
of those techniques and their vulnerability against adversarial attacks.
Confusion in the agent's decision-making process caused by these attacks can
seriously affect the safety of the UAV. 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 the UAVs adopting them from
attacks. The agent adopts a Deep Reinforcement Learning (DRL) scheme for
guidance and planning. The agent is 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. A simulated environment for UAV explainable
DRL-based planning and guidance, including obstacles and adversarial attacks,
is built. The adversarial attacks are generated by the Basic Iterative Method
(BIM) algorithm and reduced obstacle course completion rates from 97\% to 35\%.
Two adversarial attack detectors are proposed to counter this reduction. The
first one is a Convolutional Neural Network Adversarial Detector (CNN-AD),
which achieves accuracy in the detection of 80\%. The second detector utilises
a Long Short Term Memory (LSTM) network. It achieves an accuracy of 91\% with
faster computing times compared to the CNN-AD, allowing for real-time
adversarial detection.

采用可解释的深度学习方法为指导和规划的无人机构建了一个仿真环境，其中包括障碍和对抗性攻击，并建立了对此的对抗性攻击检测器