000			01791nam a22001937a 4500
082			_a006.31 _bA398A
100			_aAlias, Meghna Mariam _945330
245			_aAdversarial Robustness in Covid 19 Classification _cby Meghna Mariam Alias
260			_aIIT Jodhpur _c2023 _bDepartment of Computer Science and Technology
300			_avii, 14p. _bHB
500			_aThe world has seen a health catastrophe of a kind never seen before as a result of the COVID-19 pandemic. As the coronavirus advances, researchers are concerned with devising methods to halt the pandemic and save lives. The effects of the pandemic have been mitigated in part by implementing artificial intelligence (AI). Many deep learning models have been developed over the last three years to diagnose COVID, classifying chest X-ray pictures as NORMAL or COVID-19. Many of these models are accurate. As several studies continue, it is time to analyze how well these models perform when challenged with subtle perturbations (adversarial attacks). This study investigates how the accuracy of the ResNet18 COVID-19 detection model is impacted by adversarial samples generated by the Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), DeepFool, Stabilized Medical Image Attack (SMIA), and SMIA with Gaussian Blur. The experimental results of this work show that the COVID-19 detection algorithms are susceptible to adversarial attacks, which could be dangerous if utilized to assist in clinical diagnosis. As a result, it remains uncertain how secure machine learning models are.
650			_aDepartment of Computer Science and Technology _945331
650			_aFGSM _945332
650			_aSMIA _945333
650			_aPGD _945334
650			_aMTech Theses _945335
700			_aMisra, Deepak _945336
942			_cTH
999			_c16568 _d16568