Effective payload and improved security using HMT Contourlet transform in medical image steganography
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This paper implements a novel approach for image steganography based on Hidden Markov Tree (HMT) Contourlet transform. In this paper, the biomedical image considers as a cover image and it is mapped to a specific frequency domain by applying HMT Contourlet transform. Then canny edge detection method implemented to detect the smooth edges to hide the secret data. The secret data is encrypted by using Paillier cryptosystem in a new location of the cover image. Particle Swarm Optimization (PSO) algorithm developed for the selection of the best place to locate the number of particles in a new location. The proposed method prevents the medical image from the various attacks such as rotate, crop, histogram, salt & pepper, blur and resize provides the robustness, thereby reduces to 8.19%, 10.88%, 24.03%, 15.27%, 13.21% and 14.35%. The performance measures of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) are calculated to show the better performance compared with the existing techniques.
KeywordsOptimal pixel adjustment process Hidden Markov tree Contourlet transform Particle swarm optimization Paillier cryptosystem
Compliance with ethical standards
Conflict of interest
The authors S. Jeevitha and N. Amutha Prabha declare that they have no conflict of interest.
Research involving human participants and/or animals
1) Statement of Human Rights
Ethical approval: For this type of study formal consent is not required.
2) Statement on the Welfare of Animals
Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.
Additional informed consent was not obtained from all individual participants for whom identifying information is included in this article.
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