Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Intermediate Feature Distance

• URL: http://arxiv.org/abs/2506.10459v1
• Date: Thu, 12 Jun 2025 08:08:52 GMT
• Title: Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Intermediate Feature Distance
• Authors: Chun Liu, Bingqian Zhu, Tao Xu, Zheng Zheng, Zheng Li, Wei Yang, Zhigang Han, Jiayao Wang,
• Abstract summary: Hyperspectral image (HSI) classification technologies based on Deep Neural Networks (DNNs) are vulnerable to adversarial attacks.<n>This paper proposes a novel method to enhance the transferability of the adversarial examples for HSI classification models.<n>The proposed method maintains robust attack performance even under defense strategies.
• Score: 12.577452125758368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, which pose security challenges to hyperspectral image (HSI) classification technologies based on DNNs. In the domain of natural images, numerous transfer-based adversarial attack methods have been studied. However, HSIs differ from natural images due to their high-dimensional and rich spectral information. Current research on HSI adversarial examples remains limited and faces challenges in fully utilizing the structural and feature information of images. To address these issues, this paper proposes a novel method to enhance the transferability of the adversarial examples for HSI classification models. First, while keeping the image structure unchanged, the proposed method randomly divides the image into blocks in both spatial and spectral dimensions. Then, various transformations are applied on a block by block basis to increase input diversity and mitigate overfitting. Second, a feature distancing loss targeting intermediate layers is designed, which measures the distance between the amplified features of the original examples and the features of the adversarial examples as the primary loss, while the output layer prediction serves as the auxiliary loss. This guides the perturbation to disrupt the features of the true class in adversarial examples, effectively enhancing transferability. Extensive experiments demonstrate that the adversarial examples generated by the proposed method achieve effective transferability to black-box models on two public HSI datasets. Furthermore, the method maintains robust attack performance even under defense strategies.

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