Backdoor Attacks for Remote Sensing Data with Wavelet Transform
Nikolaus Dr\"ager, Yonghao Xu, Pedram Ghamisi

TL;DR
This paper introduces a wavelet transform-based backdoor attack method for remote sensing data, enabling stealthy, invisible triggers that can deceive deep learning models in critical geoscience applications.
Contribution
The paper proposes a novel wavelet transform-based attack (WABA) that creates invisible triggers in remote sensing data, enhancing stealthiness over existing visible trigger methods.
Findings
High attack success rate on multiple remote sensing datasets
Effective for both scene classification and semantic segmentation
Stealthy triggers are less detectable due to low-frequency domain injection
Abstract
Recent years have witnessed the great success of deep learning algorithms in the geoscience and remote sensing realm. Nevertheless, the security and robustness of deep learning models deserve special attention when addressing safety-critical remote sensing tasks. In this paper, we provide a systematic analysis of backdoor attacks for remote sensing data, where both scene classification and semantic segmentation tasks are considered. While most of the existing backdoor attack algorithms rely on visible triggers like squared patches with well-designed patterns, we propose a novel wavelet transform-based attack (WABA) method, which can achieve invisible attacks by injecting the trigger image into the poisoned image in the low-frequency domain. In this way, the high-frequency information in the trigger image can be filtered out in the attack, resulting in stealthy data poisoning. Despite…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Bacillus and Francisella bacterial research · Spectroscopy Techniques in Biomedical and Chemical Research
