FEATURE DECOUPLING BASED ADVERSARIAL EXAMPLES DETECTION METHOD FOR REMOTE SENSING SCENE CLASSIFICATION

科研成果: 会议稿件论文同行评审

摘要

Deep Neural Networks (DNNs) have demonstrated remarkable effectiveness in remote sensing (RS) image processing. However, they remain vulnerable to adversarial examples, which are generated by adding tiny but purposeful perturbations to clean examples. Such vulnerabilities in critical applications like environmental monitoring and urban planning can lead to significant negative consequences. To mitigate the interference of adversarial examples on DNNs, in this paper, a feature decoupling based adversarial examples detection (FD-AED) method for RS images is proposed, where non-robust features are employed in the detection process. Specifically, a loss function is designed for the decoupler to disentangle the features into robust and non-robust features. Non-robust features are particularly useful because they often contain subtle clues that distinguish between clean and adversarial examples. By focusing on these non-robust features, the adversarial example detector can more effectively capture the differences between clean and adversarial examples. Experimental results indicate that the proposed FD-AED method effectively decouples robust and non-robust features, achieving more precise and reliable detection of adversarial examples.

源语言英语
10011-10014
页数4
DOI
出版状态已出版 - 2024
活动2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 - Athens, 希腊
期限: 7 7月 202412 7月 2024

会议

会议2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
国家/地区希腊
Athens
时期7/07/2412/07/24

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