Open Access

ARTICLE

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:

Intelligent Automation & Soft Computing 2022, 32(1), 271-284. https://doi.org/10.32604/iasc.2022.021296

Received 28 June 2021; Accepted 29 July 2021; Issue published 26 October 2021

Abstract

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.

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