RETRACTED ARTICLE: Robust image watermarking using fractional Krawtchouk transform with optimization

Ramasamy, Rajkumar; Arumugam, Vasuki

doi:10.1007/s12652-020-02379-z

Cited by 8 publications

(1 citation statement)

References 24 publications

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“…Nature Inspired Optimization (NIO) algorithms have become a very promising scheme to address this issue. Specifically, an effective fitness function can assist in generating a strong scaling factor [ 9 ]. However, it is still a challenge to achieve an optimal design for the objective function due to the inherent complex nature of the problem being involved.…”

Section: Introductionmentioning

confidence: 99%

Blind and Secured Adaptive Digital Image Watermarking Approach for High Imperceptibility and Robustness

Singh

Devi

Thakkar

et al. 2021

Entropy

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In the past decade, rapid development in digital communication has led to prevalent use of digital images. More importantly, confidentiality issues have also come up recently due to the increase in digital image transmission across the Internet. Therefore, it is necessary to provide high imperceptibility and security to digitally transmitted images. In this paper, a novel blind digital image watermarking scheme is introduced tackling secured transmission of digital images, which provides a higher quality regarding both imperceptibility and robustness parameters. A block based hybrid IWT- SVD transform is implemented for robust transmission of digital images. To ensure high watermark security, the watermark is encrypted using a Pseudo random key which is generated adaptively from cover and watermark images. An encrypted watermark is embedded in randomly selected low entropy blocks to increase the security as well as imperceptibility. Embedding positions within the block are identified adaptively using a Blum–Blum–Shub Pseudo random generator. To ensure higher visual quality, Initial Scaling Factor (ISF) is chosen adaptively from a cover image using image range characteristics. ISF can be optimized using Nature Inspired Optimization (NIO) techniques for higher imperceptibility and robustness. Specifically, the ISF parameter is optimized by using three well-known and novel NIO-based algorithms such as Genetic Algorithms (GA), Artificial Bee Colony (ABC), and Firefly Optimization algorithm. Experiments were conducted for the proposed scheme in terms of imperceptibility, robustness, security, embedding rate, and computational time. Experimental results support higher effectiveness of the proposed scheme. Furthermore, performance comparison has been done with some of the existing state-of-the-art schemes which substantiates the improved performance of the proposed scheme.

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Section: Introductionmentioning

confidence: 99%