Security authentication and tracking of unmanned moving vehicles with optical ID tags

There are many applications for secure authentication of unmanned moving vehicles (UMVs). Traditional object appearance-based detection systems might be vulnerable to adversarial optical deception (spoofing). To address this challenge, we crafted a tracking and authentication system resistant to spoofing by equipping both the unmanned moving vehicles and the optical receiver with optical ID tags. A variety of approaches may be used to generate secure optical tags, such as three-dimensional (3D) phase encoded tags or tags utilizing meta materials. The optical tag mounted on the UMVs generates a unique optical signature produced by illuminating the ID tag with a light source. At the receiver, this optical signature is optically encrypted by a proprietary optical key placed within the optical acquisition system to ensure that the signature of the authorized UMVs cannot be reverse engineered. The encrypted signature is then processed by a deep learning network, such as the UNet-ConvLSTM network, within a conditional generative adversarial network (cGAN) framework. This network is trained to authenticate the encrypted unique signature of the tag and track its location simultaneously. We experimentally evaluate the performance of the present method under three environmental conditions, such as normal light, low light, and fog. The results show that, through training for each environment, our model successfully tracks and authenticates the optical ID tags even under degraded environmental conditions. Our system might be a solution for spoof-resistant tracking and authentication of UMVs such as unmanned aerial vehicles, drones, aircraft, satellites, etc. in various applications, including defense and surveillance. To the best of our knowledge, this is the first report on secure authentication and tracking of unmanned moving vehicles with optical ID tags.