Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820857
Title: Graph spectral domain data hiding
Author: Al-khafaji, Hiba Mohammed Jaafar
ISNI:       0000 0004 9356 9414
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2020
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Abstract:
Recent years have witnessed an increase in applications such as social, transportation, and sensor networks. The authentication and protection of these networks' data have become a major concern. Since these data are spread at arbitrary positions, without following a Cartesian grid, the techniques of classical signal processing cannot be applied to these data. This thesis explores the recently advanced signal processing of graphs for spread spectrum data hiding to protect and authenticate data captured via these networks. In this research, we first explore the graph Fourier domain for data hiding. Our proposed method involves two models for reducing the embedding distortion in the host graph that results from hiding the secret data and for enhancing the robustness of the embedded data against attacks namely, noise addition and deletion of random nodes data. We consider two data hiding scenarios: non-blind and blind. The experimental results demonstrate that the proposed methods have reduced the distortion using MSE by an average of 94% and 80% for non-blind and blind algorithms, respectively. In addition, the robustness of the proposed method is enhanced using the Hamming Distance (HD) by an average of 93% and 99.8% for non-blind algorithm and by an average of 60% and 71% for blind algorithm after the additive noise and deleting nodes data, respectively. The second contribution focuses on proposing a new approach for reversible data hiding for unstructured data in the graph Fourier domain. The proposed methodology includes a model to reduce embedding distortion based on establishing the relationship between the value of the embedded bits and the MSE of the modified graph; our methodology includes another model to maximise the robustness of the embedded bits against the additive noise. The experimental results demonstrate that the proposed method outperforms the previous methods by an average of 87% and 92% in terms of the embedding distortion, and by an average of 54% and 86% in terms of the robustness against the additive noise, and by an average 97% and 99% in terms of reversibility of the original graph signal compared to the previous methods, respectively. The third contribution involves exploiting the Graph Wavelet Transform (GWT) properties for graph data hiding. We explore the graph wavelet transform for proposing data hiding methods, including irreversible and reversible data hiding, with new models that minimise distortion in the host graph (resulting from hiding the secret bits) and enhance robustness against attacks. The experimental simulations show that the proposed GWT data hiding method outperforms the original data hiding methods (without using the proposed models) by an average of 99% and 99.4% for non-blind and blind data hiding, respectively in terms of embedding distortion. The robustness of the GWT data hiding algorithms are enhanced by an average of 77%, 71%, 60% and 99% for non-blind and blind algorithms after the additive noise and deleting nodes data, respectively. Similarly, the proposed GWT reversible data hiding method has achieved better performance compared to the previous methods by an average of 68%, 82%, 78%, 92%, 95% and 99% in terms of the embedding distortion, robustness against additive noise and reversibility of the original signal, respectively.
Supervisor: Abhayaratne, Charith ; Chu, Xiaoli Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Thesis
EThOS ID: uk.bl.ethos.820857  DOI: Not available
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