Neutrosophic Eigenvalue Decomposition for Robust Zero-Watermarking

Abduldaim, Areej; Hamid, Mohammed; Obaiys, Saif

doi:10.25673/123681

Proceedings of International Conference on Applied Innovation in IT · 2026/03/31 · Vol. 14 · Issue 1 · pp. 1081–1092

Neutrosophic Eigenvalue Decomposition for Robust Zero-Watermarking

Areej M. Abduldaim, Mohammed Qasim Hamid, Saif R. Alsaffar and Suzan J. Obaiys

📄 Download PDF DOI: 10.25673/123681

Abstract

Novel membership functions are introduced in this paper to design a robust zero-watermarking framework through which the image is transformed to the neutrosophic domain. In the new proposed method, named Neutrosophic Eigenvalue Decomposition with Statistical Feature Fusion (NEVD-NSFF), the Discrete Cosine Transform (DCT) is applied to each image block, and the neutrosophic triplet (T,I,F) is computed for each one, where T denotes the truth component, I denotes indeterminacy, and F denotes falsity. The newly proposed neutrosophic membership functions are more adaptive to changes in brightness and conflicting statistical values than the traditional fuzzy or probabilistic systems. The invariant components are then obtained by applying eigenvalue decomposition to the neutrosophic covariance form, which are robust to compression, noise, and illumination distortions. On the other hand, to combine these components relying on their neutrosophic membership distributions, the Neutrosophic Statistical Feature Fusion (NSFF) mechanism is given to generate a secret signature that is guarded by using Shamir’s Secret Sharing. The overall complexity combining all phases is computed for the neutrosophic domain, eigenvalue decomposition, and statistical feature fusion and secret sharing. Experimental results show that the proposed framework achieves robustness, accuracy, and computational efficiency compared to traditional DCT-SVD and DWT-SVD utilized in zero-watermarking techniques.

Keywords

2010 AMS Mathematics Subject Classification: Eigenvalue Decomposition (EVD) 15A18 Neutrosophic Science 03B52 Statistical Feature Fusion 62H30 Discrete Cosine Transform (DCT) 42A38.

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