Frequency-Domain Quantification Adversarial Attacks Based on Remote Sensing Image Scene Classification

Deep neural networks have achieved significant success in remote sensing image scene classification. However, because of the strong transferability of adversarial samples, the vulnerability of scene classification networks based on remote sensing images cannot be ignored. To enhance the robustness of remote sensing image scene classification networks, ensure their reliability and security in various environments and conditions, and effectively improve their practical application value, this study proposes a Frequency-Domain Quantization (FDQ) adversarial attack method. First, the input image is subjected to a Discrete Cosine Transform (DCT), and a quantization filter is used in the frequency domain to effectively capture the prominent regions of key features that enable the image to be correctly classified in the frequency domain. Then, a class-based attention loss is proposed, which gradually causes the quantization filter to lose these key features that enable correct image classification, and the model's attention gradually deviates from features and regions that are completely unrelated to the original category. The proposed method uses the attention distribution of a model to implement black-box attacks at the feature level. Universal adversarial samples are obtained for remote sensing image generation by identifying common defense vulnerabilities in different networks. Experimental results demonstrate that the FDQ method can successfully attack most of the advanced deep neural networks in remote sensing image scene classification tasks. Compared with the current state-of-the-art attack methods based on remote sensing image scene classification tasks, FDQ's attack success rate based on the RegNetX-400MF architecture on the UCM and AID benchmark datasets increases by 35.43% and 23.63%, respectively. Experiments have shown that FDQ has good attack and transferability, making it more difficult for defense systems to resist.