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dc.contributor.authorTam, K
dc.contributor.authorWalter, M
dc.contributor.authorBarrett, A
dc.contributor.authorWalker, D
dc.date.accessioned2023-03-23T15:38:08Z
dc.date.available2023-03-23T15:38:08Z
dc.date.issued2023-04
dc.identifier.issn2754-6292
dc.identifier.issn2754-6292
dc.identifier.urihttps://pearl.plymouth.ac.uk/handle/10026.1/20624
dc.description.abstract

<jats:p>Contemporary maritime operations such as shipping are a vital component constituting global trade and defence. The evolution towards maritime autonomous systems, often providing significant benefits (e.g., cost, physical safety), requires the utilisation of artificial intelligence (AI) to automate the functions of a conventional crew. However, unsecured AI systems can be plagued with vulnerabilities naturally inherent within complex AI models. The adversarial AI threat, primarily only evaluated in a laboratory environment, increases the likelihood of strategic adversarial exploitation and attacks on mission-critical AI, including maritime autonomous systems. This work evaluates AI threats to maritime autonomous systems in situ. The results show that multiple attacks can be used against real-world maritime autonomous systems with a range of lethality. However, the effects of AI attacks vary in a dynamic and complex environment from that proposed in lower entropy laboratory environments. We propose a set of adversarial test examples and demonstrate their use, specifically in the marine environment. The results of this paper highlight security risks and deliver a set of principles to mitigate threats to AI, throughout the AI lifecycle, in an evolving threat landscape.</jats:p>

dc.publisherIntechOpen
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subject14 Life Below Water
dc.titleAdversarial AI Testcases for Maritime Autonomous Systems
dc.typejournal-article
plymouth.volume2
plymouth.publication-statusPublished online
plymouth.journalAI, Computer Science and Robotics Technology
dc.identifier.doi10.5772/ACRT.15
plymouth.organisational-group|Plymouth
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering|School of Engineering, Computing and Mathematics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA
plymouth.organisational-group|Plymouth|Users by role
plymouth.organisational-group|Plymouth|Users by role|Academics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA|UoA11 Computer Science and Informatics
dcterms.dateAccepted2023-03-22
dc.date.updated2023-03-23T15:37:54Z
dc.rights.embargodate2023-4-28
dc.identifier.eissn2754-6292
dc.rights.embargoperiodforever
rioxxterms.versionofrecord10.5772/ACRT.15


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