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High embedding capacity in 3D model using intelligent Fuzzy based clustering

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Abstract

High embedding capacity plays a vital role in the watermarking schemes to secure the ownership, authenticity, and copyright-related issues of 3D models. This study describes the watermark embedding using the High Embedding Capacity in 3D Models based on Intelligent Fuzzy Clustering scheme that includes standard Fuzzy C-means (HE-FCM) and Intelligent Fuzzy C-Means (HE-IFCM). Efficiency of optimized cluster segmentation is evaluated and compared with other optimization algorithms. Bit embedding intervals are identified in the resultant clusters to embed the watermark data resulting high embedding capacity. The embedded watermark can be extracted by performing the reverse operation. The embedding capacity, distortion rate and robustness are analyzed using Peak Signal to Noise Ratio (PSNR), Root Means Square Error, correlation coefficient and Hausdorff distance. This scheme is capable of embedding 3 KB to 15 KB for small models and 164KB to 820 KB for larger models achieving around 68 dB of PSNR with less distortion. HE-IFCM is successful in embedding large capacity of watermark data resulting in minimal distortion.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

    Modigari Narendra, L. Jani Anbarasi & M. Vergin Raja Sarobin

  2. Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, India

    M. L. Valarmathi

  3. Department of Artificial Intelligence Engineering, Research Center for AI and IoT, Near East University, Nicosia, Turkey

    Fadi Al-Turjman

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  1. Modigari Narendra
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  2. M. L. Valarmathi
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  3. L. Jani Anbarasi
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  4. M. Vergin Raja Sarobin
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  5. Fadi Al-Turjman
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Correspondence to Modigari Narendra.

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The authors Modigari Narendra, M L Valarmathi, L Jani Anbarasi, Vergin Raja Sarobin M, and Fadi Al-Turjman declare that there is no conflict of interest.

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Narendra, M., Valarmathi, M.L., Anbarasi, L.J. et al. High embedding capacity in 3D model using intelligent Fuzzy based clustering. Neural Comput & Applic 34, 17783–17792 (2022). https://doi.org/10.1007/s00521-022-07404-0

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  • DOI: https://doi.org/10.1007/s00521-022-07404-0

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