Game-Theoretic Defense Mechanisms Against Cross-Domain Adversarial Attacks in Multimodal Learning Systems

Abstract

The rapid integration of multimodal learning systems into critical infrastructures has raised new concerns regarding their vulnerability to cross-domain adversarial attacks. Unlike traditional adversarial threats that target unimodal data streams, cross-domain attacks exploit the complex interactions between modalities such as text, vision, and audio, amplifying the difficulty of detection and mitigation. This paper explores the application of game-theoretic defense mechanisms as a systematic framework to counteract these threats. By modeling adversarial interactions as strategic games between attackers and defenders, game theory provides predictive insights into adaptive attack strategies and offers robust defensive equilibria. We examine static and dynamic game-theoretic approaches, the role of Stackelberg games in anticipating adversarial moves, and the potential of cooperative game formulations for multimodal defense optimization. The analysis highlights both the strengths and limitations of game-theoretic frameworks while identifying open challenges in scalability, interpretability, and real-time adaptability. The findings emphasize the importance of integrating strategic reasoning into adversarial resilience research, laying the groundwork for resilient and trustworthy multimodal AI systems.