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%.
Highlights
Forging or manipulating means changing the originality of a certain element, data, or even an entity
Digital data processing has undermined the trust in digital photographs due to the development of subtler methods of forgery that present an ever-increasing threat to the credibility and accuracy of photographs
It is very important to maintain the integrity of digital images because they can be used for different purposes, in the court of law, medical field, newspapers, magazines, and many other fields [4]
Summary
Forging or manipulating means changing the originality of a certain element, data, or even an entity. Digital images can be modified by low-cost software [2]. When the use of digital data increases, new software tools for manipulating photos and photographs are being implemented. These tools are used to make forged data, where objects can be added or deleted to look like real data [5]. Active methods are used for the protection of digital data that include digital watermarking and digital signatures. Using passive methods to modify data is a great challenge in the field of digital data processing. In passive methods, there is no data pre-processing.
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