Reversible information hiding algorithm for cryptographic domain point cloud models based on prediction error expansion
In the contemporary cloud computing environment,ensuring data security while achieving lossless recovery of data is especially critical for applications such as medical diagnosis and three-dimensional geological modeling.This paper proposes an encryption-domain reversible information hiding algorithm for point cloud models based on prediction error ex-pansion,aimed at addressing this challenge.The essence of the algorithm lies in initially encrypting the point cloud model with a novel chaotic system to secure the model content before uploading it to cloud storage.Subsequently,the model verti-ces are effectively classified using a greedy algorithm,and the prediction error for each vertex is calculated.By extending the magnitude of the prediction error,secret information is securely embedded into the point cloud model.At the receiving end,the secret information is accurately extracted by comparing the magnitudes of prediction errors,and the original point cloud model is losslessly recovered.Experimental verification has demonstrated that,compared to existing technologies,the proposed algorithm significantly improves embedding performance,with an average embedding rate increase of 0.284 bits per vertex(bpv)and 0.298 bpv,respectively,thus markedly optimizing the efficiency and security of information embed-ding.More importantly,the algorithm ensures the lossless recovery capability of the point cloud model,fulfilling the re-quirement for algorithm reversibility.In conclusion,the proposed encryption-domain reversible information hiding algorithm based on prediction error expansion for point cloud models not only secures the content of the model but also effectively en-hances the performance of secret information embedding and precision of extraction.This algorithm offers a novel solution for application scenarios such as medical diagnosis and geological exploration that demand high data security and integrity resto-ration.
information hiding3D point cloud modelprediction error expansiongreedy algorithm
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