Open Access

ARTICLE

Extended Forgery Detection Framework for COVID-19 Medical Data Using Convolutional Neural Network

Sajid Habib Gill¹, Noor Ahmed Sheikh¹, Samina Rajpar¹, Zain ul Abidin², N. Z. Jhanjhi^3,*, Muneer Ahmad⁴, Mirza Abdur Razzaq¹, Sultan S. Alshamrani⁵, Yasir Malik⁶, Fehmi Jaafar⁷

1 Department of Computer Science, Shah Abdul Latif University, Khairpur, 66020, Sindh, Pakistan
2 National College of Business Administration & Economics, Lahore, 54000, Pakistan
3 School of Computer Science and Engineering, SCE, Taylor’s University, Malaysia
4 Department of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya, Malaysia
5 Department of Information Technology, College of Computer and Information Technology, Taif University, Taif, 21944, Saudi Arabia
6 Department of Computer Science, College of Engineering & Computing Sciences, New York Institute of Technology, Vancouver, Canada
7 The Computer Research Institute of Montreal, Quebec, Canada

* Corresponding Author: N. Z. Jhanjhi. Email:

Computers, Materials & Continua 2021, 68(3), 3773-3787. https://doi.org/10.32604/cmc.2021.016001

Received 17 December 2020; Accepted 15 March 2021; Issue published 06 May 2021

Abstract

Medical data tampering has become one of the main challenges in the field of secure-aware medical data processing. Forgery of normal patients’ medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways recently. Therefore, the integrity of these data can be questionable. Forgery detection is a method of detecting an anomaly in manipulated forged data. An appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original data. Convolutional neural networks (CNNs) have contributed a major breakthrough in this type of detection. There has been much interest from both the clinicians and the AI community in the possibility of widespread usage of artificial neural networks for quick diagnosis using medical data for early COVID-19 patient screening. The purpose of this paper is to detect forgery in COVID-19 medical data by using CNN in the error level analysis (ELA) by verifying the noise pattern in the data. The proposed improved ELA method is evaluated using a type of data splicing forgery and sigmoid and ReLU phenomenon schemes. The proposed method is verified by manipulating COVID-19 data using different types of forgeries and then applying the proposed CNN model to the data to detect the data tampering. The results show that the accuracy of the proposed CNN model on the test COVID-19 data is approximately 92%.

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Keywords

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