Content area

Abstract

A perceptually tuned blind digital watermarking method for color images in a hybrid lifting wavelet transform (LWT) and discrete cosine transform (DCT) domain is proposed in this work. The color watermark is scrambled by arnold transform before embedding to enhance the security level. The color host image is firstly transformed by lifting wavelet transform and the approximate sub-band is partitioned into different blocks each of size 8 x 8. The pixel values of each block are quantized using a quantization table. Each quantized-block is further decomposed by the DCT, to generate the AC coefficient matrix. Scrambled watermark is thereafter inserted into the AC coefficient matrix using the perceptually tuned dynamic embedding strength factor. The visual quality and robustness of the proposed watermarking are balanced by optimizing the value of perceptually tuned strength factor by the Artificial bee colony (ABC) optimization algorithm. Experimental results show that the color watermark is undetectable (with CPSNR > 40 dB) into the host image and robust enough (with NC > 0.9) to resist the image processing and manipulation attacks. Comparison analysis with other related watermarking methods highlights the efficacy of the proposed work.

Details

Title
Artificial bee colony based perceptually tuned blind color image watermarking in hybrid LWT-DCT domain
Author
Sharma, Sourabh 1   VIAFID ORCID Logo  ; Sharma, Harish 1 ; Sharma, Janki Ballabh 2 

 Rajasthan Technical University, Department of Computer Science & Engineering, Kota, India (GRID:grid.449434.a) (ISNI:0000 0004 1800 3365) 
 Rajasthan Technical University, Department of Electronics & Communication, Kota, India (GRID:grid.449434.a) (ISNI:0000 0004 1800 3365) 
Pages
18753-18785
Publication year
2021
Publication date
May 2021
Publisher
Springer Nature B.V.
ISSN
13807501
e-ISSN
15737721
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2529604787
Copyright
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021.