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Object Detection in Remote Sensing Images of Pine Wilt Disease Based on Adversarial Attacks and Defenses
When using deep neural networks for the unmanned aerial vehicle remote sensing image detection and recognition of pine wilt disease (PWD), it could be found that the model is vulnerable to adversarial samples and may lead to abnormal recognition results. That is, serious errors in model classification and localization can be caused by adding minor perturbations, which are difficult for the human eye to detect, to the original samples. Traditional defense strategies rely heavily on adversarial training, but this defense always lags behind the pace of attack. In order to solve this problem, based on the YOLOv5 model, an improved YOLOV5-DRCS model with an adaptive shrinkage filtering network is proposed as follows, which enables the model to maintain relatively stable robustness after being attacked: soft threshold filtering is used in the feature extraction module, the threshold value is calculated based on the adaptive structural unit for denoising, and a SimAM attention mechanism is added in the feature layer fusion so that the final result has more global attention. In order to evaluate the effectiveness of this method, the fast gradient symbol method with white-box attacks was used to conduct an attack test on the remote sensing image dataset of pine wood nematode disease. The results showed that when the number of samples increased by 40%, the average accuracy of 92.5%, 92.4%, 91.0%, and 90.1% on the counter disturbance coefficients ϵ ∈ {2,4,6,8} was maintained, respectively, indicating that the proposed method could significantly improve the robustness and accuracy of the model when faced with the challenge of counter samples.
Object Detection in Remote Sensing Images of Pine Wilt Disease Based on Adversarial Attacks and Defenses
When using deep neural networks for the unmanned aerial vehicle remote sensing image detection and recognition of pine wilt disease (PWD), it could be found that the model is vulnerable to adversarial samples and may lead to abnormal recognition results. That is, serious errors in model classification and localization can be caused by adding minor perturbations, which are difficult for the human eye to detect, to the original samples. Traditional defense strategies rely heavily on adversarial training, but this defense always lags behind the pace of attack. In order to solve this problem, based on the YOLOv5 model, an improved YOLOV5-DRCS model with an adaptive shrinkage filtering network is proposed as follows, which enables the model to maintain relatively stable robustness after being attacked: soft threshold filtering is used in the feature extraction module, the threshold value is calculated based on the adaptive structural unit for denoising, and a SimAM attention mechanism is added in the feature layer fusion so that the final result has more global attention. In order to evaluate the effectiveness of this method, the fast gradient symbol method with white-box attacks was used to conduct an attack test on the remote sensing image dataset of pine wood nematode disease. The results showed that when the number of samples increased by 40%, the average accuracy of 92.5%, 92.4%, 91.0%, and 90.1% on the counter disturbance coefficients ϵ ∈ {2,4,6,8} was maintained, respectively, indicating that the proposed method could significantly improve the robustness and accuracy of the model when faced with the challenge of counter samples.
Object Detection in Remote Sensing Images of Pine Wilt Disease Based on Adversarial Attacks and Defenses
Qing Li (author) / Wenhui Chen (author) / Xiaohua Chen (author) / Junguo Hu (author) / Xintong Su (author) / Zhuo Ji (author) / Yingjun Wu (author)
2024
Article (Journal)
Electronic Resource
Unknown
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