From Coarse to Fine: A Training-Free Framework for Hierarchical Traceability of Adversarial Attacks in Remote Sensing Systems

Adversarial attacks present a severe threat to the trustworthiness of remote sensing uncrewed systems. Existing detection methods are mostly limited to binary classification, lacking fine-grained traceability and relying heavily on adversarial example (AE) training. To overcome these challenges, we propose a training-free hierarchical adversarial attack traceability framework leveraging the zero-shot transfer ability of contrastive language-image pretraining (CLIP), eliminating the dependency on AE training. For the first time, we establish a multigranularity attack propagation path analysis system. Specifically, the framework leverages CLIP for fine-grained adversarial attack classification without fine-tuning, constructing a hierarchical traceability system (HTS) from coarse-grained to fine-grained semantic levels. Based on Bayesian inference, we design a hierarchical probability fusion method that improves coarse-grained results through maximum a posteriori (MAP) estimation of fine-grained classifiers, collaboratively optimizing hierarchical probability distributions. To capture frequency-domain characteristics of adversarial attacks, we propose a frequency-aware kernel approximation combining high-frequency-enhanced radial basis function (RBF) kernels with ridge regression, improving sensitivity to subtle perturbations in the reproducing kernel Hilbert space (RKHS). This study establishes a comprehensive traceability system for attack propagation chains, providing an interpretable, training-free paradigm for remote sensing security, significantly enhancing fine-grained detection capabilities of uncrewed systems. Extensive experiments on two public remote sensing datasets, RSSSN7 and DWEFS, and one proprietary dataset demonstrate that our method significantly improves adversarial classification accuracy across two victim models, achieving gains of 17.13%/18.89%/13.44% and 17.91%/19.58%/12.17%, respectively, compared with state-of-the-art (SOTA) training-free baselines.