MTFM: Multi-Teacher Feature Matching for Cross-Dataset and Cross-Architecture Adversarial Robustness Transfer in Remote Sensing Applications

Remote sensing plays a critical role in environmental monitoring, land use analysis, and disaster response by enabling large-scale, data-driven observation of Earth’s surface. Image classification models are central to interpreting remote sensing data, yet they remain vulnerable to adversarial attacks that can mislead predictions and compromise reliability. While adversarial training improves robustness, the challenge of transferring this robustness across models and domains remains underexplored. This study investigates robustness transfer as a defense strategy, aiming to enhance the resilience of remote sensing classifiers against adversarial patch attacks. We propose a novel Multi-Teacher Feature Matching (MTFM) framework to align feature spaces between clean and adversarially robust teacher models and the student model, aiming to achieve an optimal trade-off between accuracy and robustness against adversarial patch attacks. The proposed method consistently outperforms traditional standard models and matches—or in some cases, surpasses—conventional defense strategies across diverse datasets and architectures. The MTFM approach also supersedes the self-attention module-based adversarial robustness transfer. Importantly, it achieves these gains with less training effort compared to traditional adversarial defenses. These results highlight the potential of robustness-aware knowledge transfer as a scalable and efficient solution for building resilient geospatial AI systems.