###### Abstract  
The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in several studies to detect COVID-19 symptoms due to the success in chest radiography image classification, cost efficiency, lack of expert radiologists, and the need for faster processing in the pandemic area. Most of the promising algorithms proposed in different studies are based on pre-trained deep learning models. Such open-source models and lack of variation in the radiology image-capturing environment make the diagnosis system vulnerable to adversarial attacks such as fast gradient sign method (FGSM) attack. This study therefore explored the potential vulnerability of pre-trained convolutional neural network algorithms to the FGSM attack in terms of two frequently used models, VGG16 and Inception-v3. Firstly, we developed two transfer learning models for X-ray and CT image-based COVID-19 classification and analyzed the performance extensively in terms of accuracy, precision, recall, and AUC. Secondly, our study illustrates that misclassification can occur with a very minor perturbation magnitude, such as 0.009 and 0.003 for the FGSM attack in these models for X-ray and CT images, respectively, without any effect on the visual perceptibility of the perturbation. In addition, we demonstrated that successful FGSM attack can decrease the classification performance to 16.67% and 55.56% for X-ray images, as well as 36% and 40  
(PCR) method is the gold standard approach used to detect COVID-19 cases by detecting SARS-CoV-2 RNA from collected samples or through pharyngeal or nasopharyngeal swabs [1]. Radiography examination, another screening method for COVID-19, conducts chest radiography imaging such as X-ray or computed tomography (CT) imaging, and radiologists can analyze them for visual signs of SARS-CoV-2 viral infection. PCR testing has high sensitivity, but it is a laborious, complicated, costly, and very time-consuming manual process. Strict requirements for laboratory environment and limited supply delay the precise diagnosis of suspected patients, posing challenges to preventing the spread of the infection, mostly at the epidemic zone. On the other hand, the radiography examination is faster and more widely available [2]. Thus, a chest X-ray or CT imaging can be performed for a patient. If radiography and clinical situations are normal, patients can go home and wait for the etiological test results. However, patients may be admitted to the hospital if the X-ray film shows pathological conditions. Thus, radiography examination is very useful approach over the PCR testing and comes out with higher sensitivity in some cases [3]. Several recent reports have emphasized chest CT as a key component of the diagnostic procedure for suspected COVID-19-affected patients [4; 5; 6]. Abnormalities in chest radiography images exist for COVID-19-infected people [4; 7]; therefore, it is a vital tool in epidemic areas for COVID-19 screening [8]. The visual indicators can be elusive; therefore, the lack of expert radiologists is a bottleneck to interpret the radiography images. Medical diagnosis uses computer vision algorithms without the input of a human clinician in many countries [9], and deep learning (DL) has been used successfully with remarkable performance for the automatic diagnostics of diseases, including lung diseases [10; 11]. Computer-aided diagnostic systems can support radiologists in the more accurate and faster interpretation of radiography images for COVID-19 detection. Many proposed deep learning-based artificial intelligence (AI) systems have presented promising accuracy to detect COVID-19 symptoms in radiography imaging [12; 13]. The Pre-trained Inception model was utilized after fine-tuning followed by a fully connected network to classify viral pneumonia and COVID-19 [14]. Along the same path, [PERSON] et al.