Integrating Fresnelet Transform and Spiking Cortical Model for robust medical image cryptosystem in zero-watermarking

Medical imaging is essential in modern healthcare as it enables accurate diagnosis, planning of therapies, and monitoring of various medical problems. The security and integrity of medical images have become of utmost importance due to the growing dependence on digital imaging technologies. Zero-watermarking is a technique used in digital image processing where the watermark is not embedded into the medical image instead, a key is generated for security purposes to achieve the information process. In the context of medical images, preserving visual fidelity and diagnostic accuracy is of utmost importance, making zero-watermarking an attractive approach for safeguarding sensitive healthcare data. This paper proposes a zero-watermarking system that utilizes the Frensnelet Transform and Spiking Cortical Model (FT-SCM) for extracting image features and enhancing the system’s resilience against various attacks. The proposed system uses FT-SCM techniques to generate a perceptual hash of size 1 × 64. The proposed cryptosystem is used to encrypt the watermark for better security. Later, the encrypted watermark and perceptual hash are merged to create a zero-watermark image (key). The proposed system yields an average PSNR, SSIM, and NC values of 57.07 dB, 0.99, and 0.75, respectively, which demonstrates an improved performance from the state-of-the-art zero-watermarking approaches. Also, the proposed cryptosystem is analysed and compared using security metrics. Security assessments demonstrate the proposed image cryptosystem approach’s efficiency, security, and realizability because of key generation in the confusion and diffusion process, which gives a chaotic nature compared to state-of-the-art approaches encryption methods.