Dual Watermarking for Security of COVID-19 Patient Record

In recent years, smart healthcare systems have gained popularity due to the ease of sharing e-patient records over the open network. The issue of maintaining the security of these records has attracted many researchers. Thus, robust and dual watermarking based on redundant discrete wavelet transform (RDWT), Hessenberg Decomposition (HD), and randomized singular value decomposition (RSVD) are put forward for CT scan images of COVID-19 patients. To ensure a high level of authentication, multiple watermarks in form of Electronic Patient Record (EPR) text and medical image are embedded in the cover. The EPR is encoded via turbo code to reduce /eliminate the channel noise if any. Further, both imperceptibility and robustness are achieved by a fuzzy inference system, and the marked image is encrypted using a lightweight encryption technique. Moreover, the extracted watermark is denoised using the concept of deep neural network (DNN) to improve its robustness. Experiment results and performance analyses verify the proposed dual watermarking scheme.

Automated summary

Smart healthcare systems have become popular for sharing e-patient records easily, but the security of these records is a concern. This study proposes a dual watermarking scheme based on RDWT, HD, and RSVD for CT scan images of COVID-19 patients. Multiple watermarks in the form of EPR text and medical image are embedded for authentication, and Turbo coding is used to reduce/eliminate channel noise. Fuzzy inference system achieves both imperceptibility and robustness, and a lightweight encryption technique is applied for image encryption. The extracted watermark is denoised with DNN to improve robustness. Experiment results confirm the effectiveness of the proposed scheme.