Autonomous Unmanned Aerial Vehicles (UAVs) have become essential tools in defense, law enforcement, disaster response, and product delivery. These autonomous navigation systems require a wireless communication network, and of late are deep learning based. In critical scenarios such as border protection or disaster response, ensuring the secure navigation of autonomous UAVs is paramount. But, these autonomous UAVs are susceptible to adversarial attacks through the communication network or the deep learning models - eavesdropping / man-in-the-middle / membership inference / reconstruction. To address this susceptibility, we propose an innovative approach that combines Reinforcement Learning (RL) and Fully Homomorphic Encryption (FHE) for secure autonomous UAV navigation. This end-to-end secure framework is designed for real-time video feeds captured by UAV cameras and utilizes FHE to perform inference on encrypted input images. While FHE allows computations on encrypted data, certain computational operators are yet to be implemented. Convolutional neural networks, fully connected neural networks, activation functions and OpenAI Gym Library are meticulously adapted to the FHE domain to enable encrypted data processing. We demonstrate the efficacy of our proposed approach through extensive experimentation. Our proposed approach ensures security and privacy in autonomous UAV navigation with negligible loss in performance.

翻译：自主无人机已成为国防、执法、灾害响应及物流配送等领域的关键工具。此类自主导航系统依赖无线通信网络，且近年来多基于深度学习技术。在边境防护或灾害响应等关键场景中，确保自主无人机的安全导航至关重要。然而，这些自主无人机易受通信网络或深度学习模型的对抗性攻击威胁，包括窃听、中间人攻击、成员推断及数据重构等。针对该脆弱性，我们提出一种创新方法，融合强化学习与全同态加密技术以保障自主无人机导航的安全性。该端到端安全框架专为无人机摄像头实时视频流设计，利用全同态加密对加密输入图像进行推理。尽管全同态加密支持对加密数据的计算操作，但部分计算算子尚未被实现。我们通过精细适配卷积神经网络、全连接神经网络、激活函数及OpenAI Gym库至全同态加密域，实现了加密数据处理功能。大量实验验证了所提方法的有效性。该方法在确保自主无人机导航安全性与隐私性的同时，性能损失可忽略不计。