Securing AI Models Against Adversarial Attacks in Military Surveillance Systems
Year: 2025
Type: article
Abstract: The integration of artificial intelligence (AI) models in military surveillance systems has revolutionized modern defense capabilities, enabling real-time threat detection, target identification, and strategic intelligence gathering. However, these systems face unprecedented vulnerabilities through adversarial attacks that can compromise their effectiveness and potentially endanger national security. This paper examines the critical security challenges facing AI-powered military surveillance systems, analyzes various adversarial attack vectors, and proposes comprehensive defense mechanisms to ensure operational integrity. Through systematic analysis of current threats and emerging solutions, we demonstrate that a multi-layered security approach combining adversarial training, robust model architectures, and real-time monitoring can significantly enhance the resilience of military AI systems against sophisticated attacks.
Authors: Girei, Abdullahi Abubakar, Abraham, Felix, Majekodunmi, Abiola Olusola
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7107878833
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