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AI Robustness Against Attacks in City-Scale Autonomous Drone Deployments

Year: 2024

Type: article

Abstract: 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.

Source: Computer

DOI: 10.1109/mc.2024.3461841

Authors: Abdul-Rasheed Ottun, Adeyinka Akintola, Mohan Liyanage, Michell Boerger, Pan Hui, Sasu Tarkoma, Nikolay Tcholtchev, Petteri Nurmi, Huber Flores

Institutions: University of Tartu, Fraunhofer Institute for Open Communication Systems, University of Helsinki, Hong Kong University of Science and Technology

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Horizon 2020

Award: G7711852765

OpenAlex ID: W4404410965

FWCI: 0.443

Cites: 18

Cited by: 3

Related to: 10