Deep-neural-network-based multiple-source remote sensing image recognition systems have been widely used in many military scenarios, such as in aerospace intelligence reconnaissance, unmanned aerial vehicles for autonomous environmental cognition, and multimode automatic target recognition systems. Deep learning models rely on the assumption that the training and testing data are from the same distribution. However, these models show poor performance under common corruption or adversarial attacks. In the remote sensing community, the adversarial robustness of deep-neural-network-based recognition models have not received much attention, thence increasing the risk for many security-sensitive applications. This article evaluates the adversarial robustness of deep-neural-network-based recognition models for multiple-source remote sensing images. First, we discuss the incompleteness of deep learning theory and reveal the presence of great security risks. The independent identical distribution assumption is often violated, and the system performance cannot be guaranteed under adversarial scenarios. The whole process chain of a deep-neural-network-based image recognition system is then analyzed for its vulnerabilities. Second, we introduce several representative algorithms for adversarial example generation under both the white- and black-box settings. The gradient-propagation-based visualization method is also proposed for analyzing adversarial attacks. We perform a detailed evaluation of nine deep neural networks across two publicly available remote sensing image datasets. Both optical remote sensing and SAR remote sensing images are used in our experiments. For each model, we generate seven perturbations, ranging from gradient-based optimization to unsupervised feature distortion, for each testing image. In all cases, we observe a significant reduction in average classification accuracy between the original clean data and their adversarial images. Apart from adversarial average recognition accuracy, feature attribution techniques have also been adopted to analyze the feature diffusion effect of adversarial attacks, hence contributing to the present understanding of the vulnerability of deep learning models. Experimental results demonstrate that all deep neural networks have suffered great losses in classification accuracy when the testing images are adversarial examples. Understanding such adversarial phenomena improves our understanding of the inner workings of deep learning models. Additional efforts are needed to enhance the adversarial robustness of deep learning models. © 2023 National Remote Sensing Bulletin
