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Existing copyright protection schemes for 3D models of oblique photography predominantly utilize digital watermarking technology, most of which suffer from shortcomings such as the potential false accusation of legitimate users and the inability to effectively trace unauthorized distributors. In response to these challenges, this article proposes a homomorphic encryption domain asymmetric fingerprinting scheme for 3D models of oblique photography. Initially, the fingerprints to be embedded are collaboratively generated by the transacting parties, with the publisher remaining unaware of the user's actual fingerprint. This approach ensures the effective implementation of the asymmetric nature of the fingerprinting scheme. Subsequently, potential geometric and non‐geometric attacks are considered prior to fingerprint embedding. Robustness is enhanced for the proposed solution through relative coordinate transformations and normalization procedures. Ultimately, the embedding and extraction of fingerprints are achieved through the application of an effective bit parity check method, in conjunction with the vertex‐paradigm projection length mapping mechanism and the Paillier homomorphic encryption system, minimizing embedding distortion while ensuring data security. Experimental results demonstrate that the proposed solution exhibits satisfying robustness against common manipulations, such as cropping, simplification, and geometric attacks. Additionally, the scheme effectively safeguards legitimate users from false accusations and accurately traces maliciously authorized users. [ABSTRACT FROM AUTHOR]

With the swift progression of three-dimensional (3D) modeling and multimedia technology, the unauthorized duplication and manipulation of 3D point cloud data has become more prevalent. Existing watermarking algorithms designed for 3D point cloud data lack robustness against rotation, cropping, and random point addition attacks. To address the aforementioned issues, we propose a robust watermarking algorithm based on the Mahalanobis distance (MD) and feature point extraction, including zero-watermarking algorithm based on MD and watermarking algorithm based on the Intrinsic Shape signatures (ISS) feature points. Firstly, calculate the MD of point cloud data and use it to construct feature matrix. A zero-watermark image is constructed through the XOR operation of the feature matrix and the copyright information matrix. Secondly, ISS feature points can be extracted from point cloud data, which using the X and Y coordinates of the feature points as indexes. The color information of the feature points is used as host data to embed the watermark. The scale invariance of MD and the stability of ISS feature points augment the robustness of the algorithm. Experimental results demonstrate that the scheme we propose exhibits strong robustness against geometric attacks, simplification attacks, cropping attacks, reordering, and noise attacks while ensuring point cloud data coordinates are lossless. [ABSTRACT FROM AUTHOR]