Universal Adversarial Perturbation Attack on the Inception-Resnet-v1 model and the Effectiveness of Adversarial Retraining as a Suitable Defense Mechanism
4th International Conference on Innovative Trends in Information Technology, ICITIIT 2023
; 2023.
Article
in English
| Scopus | ID: covidwho-2303387
ABSTRACT
In this study, we analyse the impact of the Universal Adversarial Perturbation Attack on the Inception-ResNet-v1 model using the lung CT scan dataset for COVID-19 classification and the retinal OCT scan dataset for Diabetic Macular Edema (DME) classification. The effectiveness of adversarial retraining as a suitable defense mechanism against this attack is examined. This study is categorised into three sections-The implementation of the Inception-ResNet-v1 model, the effect of the attack and the adversarial retraining. © 2023 IEEE.
adversarial retraining; deep convolutional neural networks; Inception-ResNet-v1; universal adversarial perturbation attack; Computerized tomography; Convolutional neural networks; Deep neural networks; Medical imaging; Network security; Convolutional neural network; CT-scan; Deep convolutional neural network; Defence mechanisms; Lung CT; Macular edema; Classification (of information)
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
4th International Conference on Innovative Trends in Information Technology, ICITIIT 2023
Year:
2023
Document Type:
Article
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