FGSM Adversarial Example Generation Method Based on Target Region Constraint and Cross-Model Gradient Fusion
Deep learning-based object detection models are widely applied in remote sensing fields such as remote sensing image interpretation and maritime surveillance, yet their poor robustness against adversarial perturbations has become a critical security risk for the deployment of remote sensing vision systems. Existing FGSM-based adversarial example generation methods rely on gradients from a single model and apply perturbations across the entire image, suffering from insufficient cross-model attack transferability, low perturbation concealment and utilization efficiency, and thus failing to adapt to the characteristics of remote sensing images including large target scale variations and complex backgrounds. To address this, this paper proposes a target-region constrained cross-model FGSM adversarial example generation method for remote sensing images, which fuses the loss function gradients of YOLOv5 and RetinaNet to generate adversarial directions, and combines a region constraint mechanism based on detection box dilation to confine perturbations to targets and their surrounding areas. Experiments on the SSDD remote sensing ship dataset demonstrate that, compared with the traditional single-model FGSM, the proposed method significantly reduces the recall rate of remote sensing detection models while achieving better performance in the SSIM metric. It realizes an effective trade-off between attack intensity and visual concealment, verifying its feasibility and effectiveness in the remote sensing domain.