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Alpha Fusion Adversarial Attack Analysis Using Deep Learning

Mohibullah Khan1, Ata Ullah1, Isra Naz2, Sajjad Haider1, Nz Jhanji3,*, 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: email

Computer Systems Science and Engineering 2023, 46(1), 461-473.


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.


Cite This Article

M. Khan, A. Ullah, I. Naz, S. Haider, N. Jhanji et al., "Alpha fusion adversarial attack analysis using deep learning," Computer Systems Science and Engineering, vol. 46, no.1, pp. 461–473, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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