Zero watermarking algorithm for BIM data based on distance partitioning and local feature

ABSTRACT

Zero watermarking constructs the watermark information according to the characteristics of the original data, without changing the data structure and data accuracy. Maintaining high data accuracy is the premise of building information modelling (BIM) usability, so zero watermarking is a hotspot in the research of BIM data security protection. BIM model is a type of 3D model, however, most of the existing zero watermarking algorithms for 3D models are difficult to be better applied to BIM data due to data structure differences. To solve this problem, a zero-watermarking algorithm for BIM data based on distance partitioning and skewness measure is proposed. Firstly, after spatial partitioning based on element paradigm value, the mapping relationship between different partitions and watermarking bits is established. Then, the skewness of elements is calculated, and the skewness measure sign is used as the feature to obtain the binary sequence. Finally, the dissimilarity operation is performed on the binary sequence and the original watermarking sequence which was disordered to construct the zero watermark of the BIM data. The experimental results show that the zero watermarks constructed from different BIM data are unique and robust to translation, rotation, element deletion, element addition, and format conversion attacks. In addition, the superiority of this paper’s algorithm over the comparison algorithm in terms of robustness is compared. Therefore, the proposed algorithm can effectively provide technical support for BIM data copyright protection.

KEYWORDS:

• BIM data zero-watermarking
• distance partitioning
• skewness measure

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Qianwen Zhou, Changqing Zhu, Na Ren, and Qifei Zhou performed the conceptualization; Qianwen Zhou, Changqing Zhu, and Na Ren performed the methodology; Qianwen Zhou and Na Ren performed the validation; Qianwen Zhou and Changqing Zhu performed the formal analysis; Qianwen Zhou performed the original writing; Qianwen Zhou and Na Ren performed the review & editing; Qianwen Zhou and Changqing Zhu performed the visualization; Changqing Zhu, Na Ren, and Qifei Zhou performed the supervision; Changqing Zhu, Na Ren, and Qifei Zhou performed the funding acquisition.

Data availability statement

The data and codes that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [42071362] and [42301482]; the National Key Research and Development Program of China [2023YFB3907100]; and Open Project of Hunan Engineering Research Center of Geographic Information Security and Application [HNGISA2023002].