增强自主无人机导航的隐私与安全性
Enhancing Privacy and Security of Autonomous UAV Navigation
AI总结：
本文提出结合强化学习与全同态加密（FHE）的端到端安全框架，通过对加密图像执行推理并适配神经网络与OpenAI Gym库，在几乎不损失性能的前提下有效防御窃听、中间人等对抗性攻击，保障自主无人机导航的隐私与安全。
摘要：
自主无人机（UAV）已成为国防、执法、灾难响应和产品配送等领域不可或缺的工具。这些自主导航系统需要无线通信网络，并且近来是基于深度学习的。在边境保护或灾难响应等关键场景中，确保自主无人机的安全导航至关重要。然而，这些自主无人机容易通过通信网络或深度学习模型遭受对抗性攻击——窃听/中间人攻击/成员推断攻击/重构攻击。为了解决这一弱点，我们提出了一种结合强化学习（RL）和全同态加密（FHE）的创新方法，用于安全的自主无人机导航。这种端到端的安全框架专为无人机摄像头捕获的实时视频流而设计，并利用FHE对加密输入图像执行推理。尽管FHE允许对加密数据进行计算，但某些计算算子尚未实现。卷积神经网络、全连接神经网络、激活函数和OpenAI Gym库被精心适配到FHE域中，以实现加密数据处理。我们通过广泛的实验证明了所提方法的有效性。我们提出的方法确保了自主无人机导航的安全性和隐私性，且性能损失微乎其微。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
