Plant Disease Classification and Adversarial Attack Using SimAM-EfficientNet and GP-MI-FGSM

Haotian You, Yufang Lu () and Haihua Tang
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Haotian You: School of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China
Yufang Lu: School of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China
Haihua Tang: School of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China

Sustainability, 2023, vol. 15, issue 2, 1-18

Abstract: Plant diseases have received common attention, and deep learning has also been applied to plant diseases. Deep neural networks (DNNs) have achieved outstanding results in plant diseases. Furthermore, DNNs are very fragile, and adversarial attacks in image classification deserve much attention. It is important to detect the robustness of DNNs through adversarial attacks. The paper firstly improves the EfficientNet by adding the SimAM attention module. The SimAM-EfficientNet is proposed in this paper. The experimental results show that the accuracy of the improved model on PlantVillage reaches 99.31%. The accuracy of ResNet50 is 98.33%. The accuracy of ResNet18 is 98.31%. The accuracy of DenseNet is 98.90%. In addition, the GP-MI-FGSM adversarial attack algorithm improved by gamma correction and image pyramid in this paper can increase the success rate of attack. The model proposed in this paper has an error rate of 87.6% whenattacked by the GP-MI-FGSM adversarial attack algorithm. The success rate of GP-MI-FGSM proposed in this paper is higher than other adversarial attack algorithms, including FGSM, I-FGSM, and MI-FGSM.

Keywords: plant diseases; DNN; SimAM; efficientNet; FGSM; gamma correction; image pyramid (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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