Fusing Physics-Driven Strategies and Cross-Modal Adversarial Learning: Toward Multi-Domain Applications

TL;DR

This paper reviews how integrating physics-driven strategies with cross-modal adversarial learning can improve multi-domain applications, addressing challenges like data scarcity and modality discrepancies, and highlights future unified frameworks for robust multi-modal systems.

Contribution

It systematically analyzes the synergy between physics-based methods and adversarial learning, proposing a unified framework for enhanced multi-modal application robustness.

Findings

Physics-based optimization improves adversarial perturbation interpretability.

Cross-modal adversarial learning enhances multi-domain task performance.

Unified frameworks can handle complex scenarios with physical consistency.

Abstract

The convergence of cross-modal adversarial learning and physics-driven methods represents a cutting-edge direction for tackling challenges in complex multi-modal tasks and scientific computing. This review focuses on systematically analyzing how these two approaches can be synergistically integrated to enhance performance and robustness across diverse application domains. By addressing key obstacles such as modality discrepancies, limited data availability, and insufficient model robustness, this paper highlights the role of physics-based optimization frameworks in facilitating efficient and interpretable adversarial perturbation generation. The review also explores significant advancements in cross-modal adversarial learning, including applications in tasks such as image cross-modal retrieval (e.g., infrared and RGB matching), scientific computing (e.g., solving partial differential equations), and optimization under physical consistency constraints in vision systems. By examining theoretical foundations and experimental outcomes, this study demonstrates the potential of combining these approaches to handle complex scenarios and improve the security of multi-modal systems. Finally, we outline future directions, proposing a novel framework that unifies physical principles with adversarial optimization, providing a pathway for researchers to develop robust and adaptable cross-modal learning methods with both theoretical and practical significance.