DeepSIM: GPS Spoofing Detection on UAVs using Satellite Imagery Matching

Nian Xue, Liang Niu, Xianbin Hong, Zhen Li, Larissa Hoffaeller, Christina Pöpper

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Unmanned Aerial Vehicles (UAVs), better known as drones, have significantly advanced fields such as aerial surveillance, military reconnaissance, cadastral surveying, disaster monitoring, and delivery services. However, UAVs rely on civilian (unauthenticated) GPS for navigation which can be trivially spoofed. In this paper, we present DeepSIM, a satellite imagery matching approach to detect GPS spoofing attacks against UAVs based on deep learning. We make use of the camera(s) a typical UAV is equipped with, and present a system that compares historical satellite images of its GPS-based position (spaceborne photography) with real-time aerial images from its cameras (airborne imagery). Historical images are taken from, e. g., Google Earth or NASA WorldWind. To detect GPS spoofing attacks, we investigate different deep neural network models that compare the real-time camera images with the historical satellite images. To train and test the models, we have constructed the SatUAV dataset (consisting of 967 image pairs), partially by using real UAVs such as the DJI Phantom 4 Advanced. Real-world experimental results show that our best model has a success rate of about 95% in detecting GPS spoofing attacks within less than 100 milliseconds. Our approach does not require any modification of the existing GPS infrastructures and relies only on public satellite imagery, making it a practical solution for many everyday scenarios.

Original languageEnglish (US)
Title of host publicationProceedings - 36th Annual Computer Security Applications Conference, ACSAC 2020
PublisherAssociation for Computing Machinery
Number of pages16
ISBN (Electronic)9781450388580
StatePublished - Dec 7 2020
Event36th Annual Computer Security Applications Conference, ACSAC 2020 - Virtual, Online, United States
Duration: Dec 7 2020Dec 11 2020

Publication series

NameACM International Conference Proceeding Series


Conference36th Annual Computer Security Applications Conference, ACSAC 2020
Country/TerritoryUnited States
CityVirtual, Online


  • deep learning
  • GPS spoofing detection
  • neural networks
  • UAV

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software


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