6533b855fe1ef96bd12b09a9

RESEARCH PRODUCT

A Robust Blind 3-D Mesh Watermarking Technique Based on SCS Quantization and Mesh Saliency for Copyright Protection

Mohammed El HassouniHocine CherifiAladine ChetouaniMohamed El HazitiMohamed Hamidi

subject

FOS: Computer and information sciencesComputer Science - Cryptography and SecurityComputer science[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingQuantization (signal processing)Data_MISCELLANEOUS020207 software engineeringWatermark02 engineering and technologyGraphics (cs.GR)Computer Science - Graphics[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingComputer engineering0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingPolygon meshVertex normalQuantization (image processing)Digital watermarkingCryptography and Security (cs.CR)ComputingMilieux_MISCELLANEOUSSmoothing

description

Due to the recent demand of 3-D meshes in a wide range of applications such as video games, medical imaging, film special effect making, computer-aided design (CAD), among others, the necessity of implementing 3-D mesh watermarking schemes aiming to protect copyright has increased in the last decade. Nowadays, the majority of robust 3-D watermarking approaches have mainly focused on the robustness against attacks while the imperceptibility of these techniques is still a serious challenge. In this context, a blind robust 3-D mesh watermarking method based on mesh saliency and scalar Costa scheme (SCS) for Copyright protection is proposed. The watermark is embedded by quantifying the vertex norms of the 3-D mesh by SCS scheme using the vertex normal norms as synchronizing primitives. The choice of these vertices is based on 3-D mesh saliency to achieve watermark robustness while ensuring high imperceptibility. The experimental results show that in comparison with the alternative methods, the proposed work can achieve a high imperceptibility performance while ensuring a good robustness against several common attacks including similarity transformations, noise addition, quantization, smoothing, elements reordering, etc.

https://univ-orleans.hal.science/hal-02384231