Tunable Q-Factor Wavelet Transform-Based Robust Image Watermarking Scheme Using Logistic Mapping and Antlion Optimization

In recent times, data security is an emerging and highly challenging issue due to the rapid growth of digital technologies and their uses. Copyright protection can be considered as one of the major security concerns for digital data (e.g., images), and efficient security techniques are needed to pro...

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Bibliographic Details
Published in:Circuits, systems, and signal processing systems, and signal processing, 2022-11, Vol.41 (11), p.6370-6410
Main Authors: Sinhal, Rishi, Ansari, Irshad Ahmad
Format: Article
Language:English
Subjects:
Circuits and Systems
Color imagery
Copy protection
Digital data
Digital imaging
Digital watermarking
Discrete cosine transform
Electrical Engineering
Electronics and Microelectronics
Embedding
Engineering
Instrumentation
Mapping
Optimization
Q factors
Redundancy
Robustness
Security
Signal,Image and Speech Processing
Wavelet transforms
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Summary:In recent times, data security is an emerging and highly challenging issue due to the rapid growth of digital technologies and their uses. Copyright protection can be considered as one of the major security concerns for digital data (e.g., images), and efficient security techniques are needed to provide effective solutions. Digital watermarking is extensively used for protection of copyrights and ownership. This work proposes a transform domain-based robust image watermarking scheme for color and gray images. The watermarking scheme uses the concept of tunable Q-factor wavelet transform (TQWT), discrete cosine transform (DCT), logistic mapping and antlion optimization (ALO). The host image (Y channel of YCbCr color space in case of a color image) is first transformed using TQWT transform, and the low frequency subband is selected. DCT transform is applied on each elected block, and the selected DCT coefficient is modified based on the watermark bit. The logistic mapping-based approach is employed to encrypt the watermark information before embedding. The ALO optimization is used on a large data set (including different types of images) to get optimize parametric values (Q-factor, redundancy and embedding strength). Experimental results show that the scheme has significant imperceptibility, high robustness, high security, low processing time and acceptable embedding capacity. Investigational results and relative comparisons confirm that the proposed scheme has superior performance as compared to other recently proposed schemes.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-022-02090-8