Preventing Adversarial AI Attacks Against Autonomous Situational Awareness: A Maritime Case Study

• URL: http://arxiv.org/abs/2505.21609v1
• Date: Tue, 27 May 2025 17:59:05 GMT
• Title: Preventing Adversarial AI Attacks Against Autonomous Situational Awareness: A Maritime Case Study
• Authors: Mathew J. Walter, Aaron Barrett, Kimberly Tam,
• Abstract summary: Adrial artificial intelligence (AI) attacks pose a significant threat to autonomous transportation.<n>This paper addresses three critical research challenges associated with adversarial AI.<n>We propose building defences utilising multiple inputs and data fusion to create defensive components.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Adversarial artificial intelligence (AI) attacks pose a significant threat to autonomous transportation, such as maritime vessels, that rely on AI components. Malicious actors can exploit these systems to deceive and manipulate AI-driven operations. This paper addresses three critical research challenges associated with adversarial AI: the limited scope of traditional defences, inadequate security metrics, and the need to build resilience beyond model-level defences. To address these challenges, we propose building defences utilising multiple inputs and data fusion to create defensive components and an AI security metric as a novel approach toward developing more secure AI systems. We name this approach the Data Fusion Cyber Resilience (DFCR) method, and we evaluate it through real-world demonstrations and comprehensive quantitative analyses, comparing a system built with the DFCR method against single-input models and models utilising existing state-of-the-art defences. The findings show that the DFCR approach significantly enhances resilience against adversarial machine learning attacks in maritime autonomous system operations, achieving up to a 35\% reduction in loss for successful multi-pronged perturbation attacks, up to a 100\% reduction in loss for successful adversarial patch attacks and up to 100\% reduction in loss for successful spoofing attacks when using these more resilient systems. We demonstrate how DFCR and DFCR confidence scores can reduce adversarial AI contact confidence and improve decision-making by the system, even when typical adversarial defences have been compromised. Ultimately, this work contributes to the development of more secure and resilient AI-driven systems against adversarial attacks.

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