Plant Disease Classification and Adversarial Attack based CL-CondenseNetV2 and WT-MI-FGSM

Abstract

In recent years, deep learning has been increasingly used to the detection of pests and diseases. Unfortunately, deep neural networks are particularly vulnerable when attacked by adversarial examples. Hence it is vital to explore the creation of intensely aggressive adversarial examples to increase neural network robustness. This paper proposes a wavelet transform and histogram equalization-based adversarial attack algorithm: WT-MI-FGSM. In order to verify the performance of the WT-MI-FGSM, we propose a plant pests and diseases identification method based on the coordinate attention mechanism and CondenseNetV2: CL-CondenseNetV2. The accuracy of CL-CondenseNetV2 on the PlantVillage dataset is 99.45%, which indicates that the improved CondenseNetV2 model has a more significant classification performance. In adversarial sample experiments using WT-MI-FGSM and CL-CondenseNetV2, experimental results show that when CL-CondenseNetV2 is attacked by the adversarial algorithm WT-MI-FGSM, the error rate reaches 89.8%, with a higher attack success rate than existing adversarial attack algorithms. In addition, the accuracy of CL-CondenseNetV2 is improved to 99.71% by adding the adversarial samples generated by WT-MI-FGSM to the training set and performing adversarial training. The experiments demonstrate that the adversarial examples caused by WT-MI-FGSM can improve the model's performance.