Watermarking 3D Printing Data Based on Coyote Optimization Algorithm

Mouhamed, Mourad R.; Soliman, Mona; Darwish, Ashraf; Hassanien, Aboul Ella

doi:10.1007/978-3-030-59338-4_29

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…from point of view of attack applied to the model, G. Liu [15] proposes a zero-watermarking scheme for 3D models, using the Beamlet transform method to relocate the embedded positions of the watermark when 3D models after rotation attacks, as the vertex data of 3D models has no implicit order. Mourad R. Mouhamed [16] propose a 3D watermarking algorithm based on Coyote Optimization Algorithm (COA) to optimizing statistical watermark embedding for 3D models. they exhibit an intelligent layer on the watermarking process.…”

Section: Watermarking Taxonomymentioning

confidence: 99%

“…First, we evaluate the imperceptibility of the proposed methods by using the following metrics: Hausdroff distance (HD), Modified Hausdroff distance (MHD) [1], Root Mean Square Error (RMSE), and the Vertex Signal-to-Noise Ratio (VSNR) [16]. The RMSE measures the differences between the watermarked model and the original model, where the small RMSE values indicate insignificant positional changes during the watermark embedding.…”

Section: Imperceptibility Assessmentmentioning

confidence: 99%

“…The experimental results showed that the imperceptibility evaluation of both proposed methods of embedding is better than the methods of [11], the modified algorithm of [12] as well as the method of M.R. Mouhamed [16]. Attack detection sensitivity assessment were performed.…”

Section: Cte Csementioning

confidence: 99%

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A blind fragile watermarking for 3D triangular model based on curvature features and chaos sequence

mamdouh

Semary²,

Ahmed

2022

IJCI. International Journal of Computers and Information

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3D models unauthorized modification is an attractive research problem nowadays due to the widespread availability of technologies and designs on the internet. In this paper, we propose a blind fragile watermarking scheme in the spatial domain for 3D model authentication based on the curvature features of the 3D model and chaos sequence. First, we compute the curvature feature for every vertex of the 3D model, after that, the vertices are classified into 3 classes: flat, peak, and In-between vertices using K-means clustering algorithm. We suggested two methods for embedding the watermark based on the least significant bit (LSB) substitution technique; Cluster Type-based Embedding (CTE) and Cluster Size-based Embedding (CSE). The proposed methods employ a Chaos sequence generator to generate a Chaos sequence that is used to generate the embedded watermark, where the tampering region can be verified and located by the Chaos sequence-based watermark check. Many assessment methods are employed to evaluate the proposed method with various unauthorized attacks like rotation, translation, scaling, cropping, and noise addition. The experiment results show an improvement of embedding imperceptibility as well as tempered regions detection compared to existing literature works.

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Section: Watermarking Taxonomymentioning

confidence: 99%

Section: Imperceptibility Assessmentmentioning

confidence: 99%