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Restoration of Adversarial Examples Using Image Arithmetic Operations

Kazim Ali*, Adnan N. Quershi

Department of Information Technology, University of Central Punjab, Lahore, 54000, Pakistan

* Corresponding Author: Kazim Ali. Email: email

Intelligent Automation & Soft Computing 2022, 32(1), 271-284.


The current development of artificial intelligence is largely based on deep Neural Networks (DNNs). Especially in the computer vision field, DNNs now occur in everything from autonomous vehicles to safety control systems. Convolutional Neural Network (CNN) is based on DNNs mostly used in different computer vision applications, especially for image classification and object detection. The CNN model takes the photos as input and, after training, assigns it a suitable class after setting traceable parameters like weights and biases. CNN is derived from Human Brain's Part Visual Cortex and sometimes performs even better than Haman visual system. However, recent research shows that CNN Models are much vulnerable against adversarial examples. Adversarial examples are input image huts that are deliberately modified, which are imperceptible to humans, but a CNN model strongly misrepresents them. This means that adversarial attacks or examples are a serious threat to deep learning models, especially for CNNs in the computer vision field. The methods which are used to create adversarial examples are called adversarial attacks. We have proposed an easy method that restores adversarial examples, which are created due to different adversarial attacks and misclassified by a CNN model. Our reconstructed adversarial examples are correctly classified by a model again with high probability and restore the prediction of a CNN model. We will also prove that our method is based on image arithmetic operations, simple, single-step, and has low computational complexity. Our method is to reconstruct all types of adversarial examples for correct classification. Therefore, we can say that our proposed method is universal or transferable. The datasets used for experimental evidence are MNIST, FASHION-MNIST, CIFAR10, and CALTECH-101. In the end, we have presented a comparative analysis with other state-of-the methods and proved that our results are better.


Cite This Article

APA Style
Ali, K., Quershi, A.N. (2022). Restoration of adversarial examples using image arithmetic operations. Intelligent Automation & Soft Computing, 32(1), 271-284.
Vancouver Style
Ali K, Quershi AN. Restoration of adversarial examples using image arithmetic operations. Intell Automat Soft Comput . 2022;32(1):271-284
IEEE Style
K. Ali and A.N. Quershi, "Restoration of Adversarial Examples Using Image Arithmetic Operations," Intell. Automat. Soft Comput. , vol. 32, no. 1, pp. 271-284. 2022.

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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