Hessenberg Decomposition-Based Medical Image Watermarking with Its Performance Comparison by Particle Swarm and JAYA Optimization Algorithms for Different Wavelets and Its Authentication Using AES

In this paper, the medical image watermarking technique is performed by using lifting wavelet transform, singular value decomposition, and Hessenberg decomposition. Multimedia security is one of the most significant concerns in the current digital era even though it is effortless to reproduce, disseminate, and modify multimedia data. Digital watermarking is a technique for shielding critical information to protect it from illegitimate duplication and distribution. The performance of the proposed scheme is checked with several wavelets. For this purpose, various wavelets such as Haar, Daubechies, Symlet, biorthogonal, and reverse biorthogonal are applied. The scaling factor plays a significant role in watermarking. So, to get the optimized scaling factor, particle swarm optimization and JAYA optimization algorithms are used. The authentication process is done by using advanced encryption standards. The performance of the proposed algorithm is checked by applying different types of attacks such as salt and pepper noise, Gaussian noise, filtering, geometrical, and compression attacks. Peak signal-to-noise ratio, normalized correlation coefficient, mean square error, and structural similarity index measurement are calculated to evaluate the resilience and indistinguishability of the proposed watermarking algorithm. The results of the proposed technique show the significant improvement in the performance and can be used to protect the critical medical data.