  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.
