Abstract
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.
Abstract (translated)
自主无人机（UAVs）已成为军事、执法、灾难应对和产品交付等领域的不可或缺的工具。这些自主导航系统需要无线通信网络，并且最近基于深度学习。在危机场景（如边境保护或灾难应对）中，确保自主UAV的安全导航至关重要。但是，这些自主UAV通过通信网络或深度学习模型容易受到攻击 - 窃听 / 中间人攻击 / 成员推断 / 重建。为了应对这种易受攻击性，我们提出了结合强化学习（RL）和完全同态加密（FHE）的安全自主UAV导航的创新方法。这个端到端的安全框架是为由UAV相机捕获的实时视频信号设计的，并利用FHE对加密输入图像进行推理。虽然FHE允许对加密数据进行计算，但某些计算操作尚未实现。将卷积神经网络、全连接神经网络、激活函数和OpenAI Gym库细粒度地适应FHE领域，以实现加密数据处理。我们通过广泛的实验来证明我们提出的方法的有效性。我们提出的方法确保了在自主UAV导航中保护安全和隐私，同时性能损失非常小。
URL
https://arxiv.org/abs/2404.17225
