Alpha Fusion Adversarial Attack Analysis Using Deep Learning
Computer Systems Science and Engineering
; 46(1):461-473, 2023.
Artigo
em Inglês
| Scopus | ID: covidwho-2242118
ABSTRACT
The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall. © 2023 CRL Publishing. All rights reserved.
Computerized tomography; Image classification; Image segmentation; Learning systems; Long short-term memory; Neural network models; Adversarial attack; Attack analysis; Classification models; Convolutional neural network; Deep learning; F1 scores; Learning abilities; Learning models; Neural network model; Preturbation image; Convolutional neural networks; classification; preturbation images
Texto completo:
Disponível
Coleções:
Bases de dados de organismos internacionais
Base de dados:
Scopus
Idioma:
Inglês
Revista:
Computer Systems Science and Engineering
Ano de publicação:
2023
Tipo de documento:
Artigo
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