AI Robustness Against Attacks in City-Scale Autonomous Drone Deployments
We analyze the impact of poisoning attacks on autonomous drones and demonstrate how explainable artificial intelligence techniques can be employed to detect them. We then delve into the risks, opportunities, and research challenges, ultimately paving the way for city-scale deployments of autonomous drones.
Authors
- Michell Boerger (ORCID: https://orcid.org/0000-0002-5741-9043)
- Pan Hui
- Mohan Liyanage (ORCID: https://orcid.org/0000-0001-6496-1562)
- Nikolay Tcholtchev (ORCID: https://orcid.org/0000-0001-6821-4417)
- Sasu Tarkoma (ORCID: https://orcid.org/0000-0003-4220-3650)
- Abdul-Rasheed Ottun (ORCID: https://orcid.org/0000-0002-5336-1438)
- Petteri Nurmi (ORCID: https://orcid.org/0000-0001-8262-6434)
- Huber Flores (ORCID: https://orcid.org/0000-0003-4551-629X)
- Adeyinka Akintola (ORCID: https://orcid.org/0009-0004-3472-2463)
Institutions
- University of Helsinki (FI)
- Hong Kong University of Science and Technology (HK)
- Fraunhofer Institute for Open Communication Systems (DE)
- University of Tartu (EE)
- University of Hong Kong (HK)
Publication Details
- Journal
- Computer
- Published
- 2024-11-15
- DOI
- https://doi.org/10.1109/mc.2024.3461841
- Citations
- 3
- Primary Topic
- UAV Applications and Optimization
- Type
- article
- Field-Weighted Citation Impact
- 3.90
Funders
- Horizon 2020