论文标题
模糊独特的图像转换：防御对抗攻击的深入共vid型模型
Fuzzy Unique Image Transformation: Defense Against Adversarial Attacks On Deep COVID-19 Models
论文作者
论文摘要
使用在胸部X射线和CT图像上训练的深层模型对COVID-19的早期识别已从研究人员的角度引起了极大的关注，以加快鉴定活性Covid-19病例的鉴定过程。这些深层模型可以帮助患有专家或放射科医生（特别是在偏远地区）无法获得的医院。已经提出了各种深层模型来检测COVID-19情况，但是很少有工作以防止深层模型来抵抗能够通过在图像像素中使用小的扰动来欺骗深层模型的对抗攻击。本文介绍了针对对抗性攻击的Deep Covid-19模型的性能的评估。同样，它提出了一种有效而有效的模糊图像转换（FUIT）技术，将图像像素列为间隔。进一步利用在FIUT转换后获得的图像用于训练安全的深层模型，该模型可保留COVID-19病例诊断的高精度，并为对抗性攻击提供可靠的防御。实验和结果表明，提出的模型可防止对六次对抗攻击的深层模型，并保持高精度，以从胸部X射线图像和CT图像数据集对COVID-19案例进行分类。结果还建议在实际应用深层模型诊断COVID-19案件之前进行仔细检查。
Early identification of COVID-19 using a deep model trained on Chest X-Ray and CT images has gained considerable attention from researchers to speed up the process of identification of active COVID-19 cases. These deep models act as an aid to hospitals that suffer from the unavailability of specialists or radiologists, specifically in remote areas. Various deep models have been proposed to detect the COVID-19 cases, but few works have been performed to prevent the deep models against adversarial attacks capable of fooling the deep model by using a small perturbation in image pixels. This paper presents an evaluation of the performance of deep COVID-19 models against adversarial attacks. Also, it proposes an efficient yet effective Fuzzy Unique Image Transformation (FUIT) technique that downsamples the image pixels into an interval. The images obtained after the FUIT transformation are further utilized for training the secure deep model that preserves high accuracy of the diagnosis of COVID-19 cases and provides reliable defense against the adversarial attacks. The experiments and results show the proposed model prevents the deep model against the six adversarial attacks and maintains high accuracy to classify the COVID-19 cases from the Chest X-Ray image and CT image Datasets. The results also recommend that a careful inspection is required before practically applying the deep models to diagnose the COVID-19 cases.
