Optimal blind colour image watermarking based on adaptive chaotic grasshopper optimization algorithm

ABSTRACT

This paper proposes an effective optimal blind colour image watermarking based on Triangular Vertex Transform (TVT), Lifting Wavelet Transform (LWT) and Schur decomposition. Firstly, the RGB channels of the colour host image are processed with TVT to attain $U$, $V,$ and $W$ coefficients. The image quality is preserved by applying LWT on the $W$ coefficients. As the middle frequency (HL and LH) components of the LWT wavelet domain achieve much robustness, these two components are split into $4\times 4$ nonoverlapping blocks further it is processed with Schur decomposition for improving perceptual transparency. The optimal solution attained by an Adaptive Chaotic Grasshopper Optimization Algorithm (ACGOA) is used as the embedding factor to ensure the embedding strength of the watermark. The confidentiality of the watermark is improved by 2D Logistic-Modulated-Sine-Coupling-Logistic Chaotic Map (LSMCL). Subsequently, each bit of encrypted watermark image is inserted in the highest eigenvalue of upper triangular matrix of Schur decomposition using the embedding factor β obtained by ACGOA to accomplish the watermarked image. The watermark recovery is performed using the embedding factor β and the decryption process is done using LSMCL. The efficiency of the ACGOA based scheme is estimated and the highest Peak signal-to-noise ratio (PSNR) value achieved is 54.2987 dB. The Normalized Cross-Correlation (NCC) results show better reliability for different attacks including geometric attacks and the NCC value obtained is close to 1.

KEYWORDS:

• Optimal blind color image watermarking
• triangular vertex transform
• lifting wavelet transform
• schur decomposition
• adaptive chaotic grasshopper optimization algorithm
• 2D logistic-modulated-sine-coupling-logistic chaotic map
• geometric attack
• embedding factor

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

K. Prabha

K. Prabha received her MCA degree from Anna University, Chennai, India in 2014 and M.Phil. Computer Science degree from Manonmaniam Sundaranar University, Tirunelveli, India in 2017. At present, she is pursuing research in the Department of Computer Science, Nesamony Memorial Christian College, Marthandam affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, India. Her research interests include image processing, digital image watermarking and multimedia security.

I. Shatheesh Sam

Dr. I. Shatheesh Sam received Master of Computer Science and Engineering from Sathyabama University, India, in 2006. He received his Ph.D. in Information and Communication Engineering at Anna University Chennai, India in 2012. Currently, he is an Associate Professor in the Department of PG Computer Science, Nesamony Memorial Christian College affiliated to Manonmaniam Sundaranar University, Tirunelveli. His research interests include multimedia security, network security and image processing. He is a life member of CSI, ISTE, IETE and member of IEEE.