Enhancing Svd-Based Image Watermarking Strategies Based On Digital Chaos

Alshoura, Wafa’hamdan Suleiman (2022) Enhancing Svd-Based Image Watermarking Strategies Based On Digital Chaos. PhD thesis, Universiti Sains Malaysia.

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A digital image is a universal medium that carries sensitive information and has proliferated in recent years. The watermarking scheme is a technique used for protecting digital images and other content such as audio, video, and text. Image watermarking schemes have the ability to embed the owner’s information into a host image in an imperceptible manner, and can be extracted later in the detection phase. Recently, the hybrid singular value decomposition (SVD)-based watermarking schemes in the frequency domain have received considerable attention. The interest is as a result of SVD having stability and robust properties which makes it resistant to different well-known attacks. However, existing hybrid SVD schemes do not meet some critical watermarking requirements such as successful trade-offs between robustness and imperceptibility, large capacity, and high security. Hence, they produce ineffective results which are not robust and are prone to a variety of attacks. This study aims to bridge the gap by developing enhanced hybrid SVD-based image watermarking schemes to fulfil the aforementioned watermarking requirements. In the proposed schemes, random numbers and new embedding strategies are leveraged upon to address these issues as well as making the proposed schemes flexible and easy to implement. This study proposes three new schemes that can be implemented on different image formats (gray and color image). The design elements and the novel constructions incorporated in the proposed schemes makes sure that they surpass the existing schemes.

Item Type: Thesis (PhD)
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis
Depositing User: Mr Mohammad Harish Sabri
Date Deposited: 17 Aug 2023 03:39
Last Modified: 17 Aug 2023 03:39
URI: http://eprints.usm.my/id/eprint/59163

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