Open Access

ARTICLE

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

Mohibullah Khan¹, Ata Ullah¹, Isra Naz², Sajjad Haider¹, Nz Jhanji^3,*, Mohammad Shorfuzzaman⁴, Mehedi Masud⁴

1 Department of Computer Science, National University of Modern Languages, Islamabad, Pakistan
2 Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
3 School of Computer Science (SCS), Taylor’s University, Selangor, Malaysia
4 Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

* Corresponding Author: Nz Jhanji. Email:

Computer Systems Science and Engineering 2023, 46(1), 461-473. https://doi.org/10.32604/csse.2023.029642

Received 08 March 2022; Accepted 12 July 2022; Issue published 20 January 2023

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.

---

Keywords

---