A Novel Deep Learning Based Dual Watermarking System for Securing Healthcare Data

The sharing of patient information on an open network has drawn attention to the healthcare system. Security is the primary issue while sharing documents online. Thus, a dual watermarking technique has been developed to improve the security of shared data. The classical watermarking schemes are resilient to many attacks. Protecting the authenticity and copyrights of medical images is essential to prevent duplication, modification, or unauthorized distribution. This paper proposes a robust, novel dual watermarking system for securing healthcare data. Initially, watermarking is performed based on redundant lifting wavelet transform (LWT) and turbo code decomposition for COVID-19 patient images and patient text data. To achieve a high level of authenticity, watermarks in the form of encoded text data and decomposed watermark images are inserted together, and an inverse LWT is used to generate an initial watermarked image. Improve imperceptibility and robustness by incorporating the cover image into the watermarked image. Cross-guided bilateral filtering (CG_BF) improves cover image quality, while the integrated Walsh–Hadamard transform (IWHT) extracts features. The novel adaptive coati optimization (ACO) technique is used to identify the ideal location for the watermarked image in the cover image. To improve security, the watermarked image is dissected using discrete wavelet transform (DWT) and encrypted with a chaotic extended logistic system. Finally, the encrypted watermarked image is implanted in the desired place using a novel deep-learning model based on the Hybrid Convolutional Cascaded Capsule Network (HC³Net). Thus, the secured watermarked image is obtained, and the watermark and text data are extracted using the decryption and inverse DWT procedure. The performance of the proposed method is evaluated using accuracy, peak signal-to-noise ratio (PSNR), NC, and other metrics. The proposed method achieved an accuracy of 99.26%, which is greater than the existing methods.