Extended forgery detection framework for COVID-19 medical data using convolutional neural network

Gill, Sajid Habib and Sheikh, Noor Ahmed and Rajpar, Samina and ul Abidin, Zain and Jhanjhi, N. Z. and Ahmad, Muneer and Razzaq, Mirza Abdur and Alshamrani, Sultan S. and Malik, Yasir and Jaafar, Fehmi (2021) Extended forgery detection framework for COVID-19 medical data using convolutional neural network. CMC-Computers Materials & Continua, 68 (3). pp. 3773-3787. ISSN 1546-2218, DOI https://doi.org/10.32604/cmc.2021.016001.

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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%.

Item Type: Article
Funders: Computer Research Institute of Montreal, Quebec, Canada, Ministere de l'Economie et de l'Innovation, Quebec, Canada, Taif University Researchers Supporting Project, Taif University, Taif, Saudi Arabia [TURSP2020/215]
Uncontrolled Keywords: Data security; Data privacy; Medical-data forgery; COVID-19; Convolutional neural network; Machine learning; Deep learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Computer Science & Information Technology > Department of Information Science
Depositing User: Ms Zaharah Ramly
Date Deposited: 09 Jun 2022 06:53
Last Modified: 09 Jun 2022 06:53
URI: http://eprints.um.edu.my/id/eprint/34345

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