Adversarial Robust Salient Object Detection in Optical Remote Sensing Images With Implicit Feature Enhancement

Deep neural networks (DNNs) have achieved significant progress in optical remote sensing images salient object detection (ORSI-SOD) and are widely applied to various remote sensing image analysis tasks. However, few SOD models demonstrate robust performance under adversarial perturbations, which ultimately leads to a decline in detection accuracy. Moreover, most existing defense methods inject fixed Gaussian noise globally into the image. Although such approaches are easy to implement, they have several limitations in inaccurate uncertainty estimation and neglecting the unique characteristics of local salient regions. Furthermore, existing adversarial defense research rarely addresses the challenges specific to the ORSI-SOD task, leaving a gap in effective defense strategies. To tackle these issues, we propose a novel defense method, which enhances the adversarial robustness of ORSI-SOD models through implicit feature enhancement. The algorithm first proposes a two-stage strategy of reverse local noise search and forward global noise optimization, enhancing generalization ability by implicitly enhancing features to better simulate network uncertainty. Then, the algorithm proposes a global-guided texture information enhancement (GTIE) module for low-level features and a global-guided semantics information enhancement (GSIE) module for high-level features, focusing on strengthening low-level texture information and enhancing the model’s understanding of high-level contextual semantic features, respectively. This dual-module design effectively weakens the impact of adversarial noise, significantly improving the robustness and accuracy of object detection. Extensive experiments on three ORSI-SOD datasets demonstrate that our defense strategy better estimates the uncertainty, resulting in an average performance improvement of 23.2% in $F_{\beta } ^{\mathrm { max}}$ and 34.1% in $E_{\xi } ^{\mathrm { max}}$ across six ORSI-SOD models under five different adversarial attack methods. Our code will be released in the public repository at https://github.com/kexi0714/IFe.