Abstract
Transfer learning using pre-trained deep neural networks (DNNs) has been widely used for plant disease identification recently. However, pre-trained DNNs are susceptible to adversarial attacks which generate adversarial samples causing DNN models to make wrong predictions. Successful adversarial attacks on deep learning (DL)-based plant disease identification systems could result in a significant delay of treatments and huge economic losses. This paper is the first attempt to study adversarial attacks and detection on DL-based plant disease identification. Our results show that adversarial attacks with a small number of perturbations can dramatically degrade the performance of DNN models for plant disease identification. We also find that adversarial attacks can be effectively defended by using adversarial sample detection with an appropriate choice of features. Our work will serve as a basis for developing more robust DNN models for plant disease identification and guiding the defense against adversarial attacks.
Highlights
IntroductionSci. On a global scale, pathogens and pests are one major reason that reduces the yield and quality of agricultural production
We describe four popular white-box attacks considered in this study: fast gradient sign method (FGSM) [40], basic iterate method (BIM) [41], projected gradient descent (PGD) [39], and Carilini and Wagner attack (CW) [38]
Pre-trained deep neural networks (DNNs) models have been widely used in machine learning and computer vision applications including plant disease identification
Summary
Sci. On a global scale, pathogens and pests are one major reason that reduces the yield and quality of agricultural production. According to the study of [1], estimated yield losses of five major crops (wheat, rice, maize, potato, and soybean) due to pathogens and pests range from 10.1% to 41.1% globally. Identifying plant diseases in an early time can prevent further yield losses of production by informing farmers of appropriate treatment processes regarding the diagnosis. Advanced lab-based methods for plant disease diagnosis such as DNA-based and serological methods, are accurate and authentic [2]. These methods are either more time-consuming or more costly than those based on the visual observation of symptoms shown on the organs of species. Automatic identification of plant diseases using plant leaf images becomes more and more popular [7]
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