Diabetic Retinopathy (DR) is a prevalent illness associated with Diabetes
which, if left untreated, can result in irreversible blindness. Deep Learning
based systems are gradually being introduced as automated support for clinical
diagnosis. Since healthcare has always been an extremely important domain
demanding error-free performance, any adversaries could pose a big threat to
the applicability of such systems. In this work, we use Universal Adversarial
Perturbations (UAPs) to quantify the vulnerability of Medical Deep Neural
Networks (DNNs) for detecting DR. To the best of our knowledge, this is the
very first attempt that works on attacking complete fine-grained classification
of DR images using various UAPs. Also, as a part of this work, we use UAPs to
fine-tune the trained models to defend against adversarial samples. We
experiment on several models and observe that the performance of such models
towards unseen adversarial attacks gets boosted on average by $3.41$
Cohen-kappa value and maximum by $31.92$ Cohen-kappa value. The performance
degradation on normal data upon ensembling the fine-tuned models was found to
be statistically insignificant using t-test, highlighting the benefits of
UAP-based adversarial fine-tuning.

通过使用通用对抗扰动来评估医学深度神经网络对糖尿病视网膜病变（DR）的检测的脆弱性，并使用通用对抗扰动对已训练模型进行微调以抵御对抗样本，提升了平均 3.41 Cohen-kappa 值和最大 31.92 Cohen-kappa 值的性能。