Adversarial Robustness in Covid 19 Classification (Record no. 16568)

MARC details
000 -LEADER
fixed length control field 01791nam a22001937a 4500
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Item number A398A
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Alias, Meghna Mariam
245 ## - TITLE STATEMENT
Title Adversarial Robustness in Covid 19 Classification
Statement of responsibility, etc by Meghna Mariam Alias
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication IIT Jodhpur
Year of publication 2023
Name of publisher Department of Computer Science and Technology
300 ## - PHYSICAL DESCRIPTION
Number of Pages vii, 14p.
Other physical details HB
500 ## - GENERAL NOTE
General note The 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).<br/><br/>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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Department of Computer Science and Technology
Topical Term FGSM
Topical Term SMIA
Topical Term PGD
Topical Term MTech Theses
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Misra, Deepak
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis
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Withdrawn status Lost status Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Full call number Accession Number Price effective from Koha item type
        S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Reference 2024-04-01 006.31 A398A TM00503 2024-06-27 Thesis