A Novel Fragile Zero-Watermarking Algorithm for Digital Medical Images

Abstract

The wireless transmission of patients’ particulars and medical data to a specialised centre after an initial screening at a remote health facility may cause potential threats to patients’ data privacy and integrity. Although watermarking can be used to rectify such risks, it should not degrade the medical data, because any change in the data characteristics may lead to a false diagnosis. Hence, zero watermarking can be helpful in these circumstances. At the same time, the transmitted data must create a warning in case of tampering or a malicious attack. Thus, watermarking should be fragile in nature. Consequently, a novel hybrid approach using fragile zero watermarking is proposed in this study. Visual cryptography and chaotic randomness are major components of the proposed algorithm to avoid any breach of information through an illegitimate attempt. The proposed algorithm is evaluated using two datasets: the Digital Database for Screening Mammography and the Mini Mammographic Image Analysis Society database. In addition, a breast cancer detection system using a convolutional neural network is implemented to analyse the diagnosis in case of a malicious attack and after watermark insertion. The experimental results indicate that the proposed algorithm is reliable for privacy protection and data authentication.

A Comprehensive Approach to Image Protection in Digital Environments

“…Efficiency is a crucial aspect of any image processing algorithm. This is achieved by measuring the execution time of the algorithm using specific Python libraries, such as "time", to determine how quickly the watermark insertion process can be applied [6]. In addition, system resources, such as CPU and memory, are monitored using tools, such as "psutil", to optimize the algorithm's performance.…”