Adversarial Robustness in Covid 19 Classification (Record no. 16568)
[ view plain ]
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 |
Withdrawn status | Lost status | Damaged status | Not for loan | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Accession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 |