The increasing adoption of cloud computing for 3D model sharing and storage necessitates robust protection mechanisms for confidentiality and ownership verification. While encryption ensures data confidentiality, watermarking techniques are required for traceability and ownership protection. This paper presents an enhanced version of an existing homomorphic encryption-based reversible watermarking scheme that employs histogram shifting and the Paillier cryptosystem for 3D models. The original method enables watermark operations in both encrypted and clear domains but suffers from high computational complexity. Our improvement refines homomorphic encryption operations while preserving the core algorithm’s reversible properties. Experimental results demonstrate substantial computational time reductions of up to 99.9% while maintaining full reversibility, security, and watermark capacity. Our code is available for download at https://github.com/PierreMahieux/Improved_RRDH .

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An Improved Paillier-Based Reversible Watermarking Scheme for 3D Models with Reduced Complexity

  • Pierre Mahieux,
  • Mouhamadou Bamba Sakho,
  • Gouenou Coatrieux,
  • Reda Bellafqira

摘要

The increasing adoption of cloud computing for 3D model sharing and storage necessitates robust protection mechanisms for confidentiality and ownership verification. While encryption ensures data confidentiality, watermarking techniques are required for traceability and ownership protection. This paper presents an enhanced version of an existing homomorphic encryption-based reversible watermarking scheme that employs histogram shifting and the Paillier cryptosystem for 3D models. The original method enables watermark operations in both encrypted and clear domains but suffers from high computational complexity. Our improvement refines homomorphic encryption operations while preserving the core algorithm’s reversible properties. Experimental results demonstrate substantial computational time reductions of up to 99.9% while maintaining full reversibility, security, and watermark capacity. Our code is available for download at https://github.com/PierreMahieux/Improved_RRDH .