Reversible Data Hiding for Encrypted 3D Model Based on Prediction Error Expansion

Abstract EN

Since 3D models can intuitively display real-world information, there are potential scenarios in many application fields, such as architectural models and medical organ models.

However, a 3D model shared through the internet can be easily obtained by an unauthorized user.

In order to solve the security problem of 3D model in the cloud, a reversible data hiding method for encrypted 3D model based on prediction error expansion is proposed.

In this method, the original 3D model is preprocessed, and the vertex of 3D model is encrypted by using the Paillier cryptosystem.

In the cloud, in order to improve accuracy of data extraction, the dyeing method is designed to classify all vertices into the embedded set and the referenced set.

After that, secret data is embedded by expanding direction of prediction error with direction vector.

The prediction error of the vertex in the embedded set is computed by using the referenced set, and the direction vector is obtained according to the mapping table, which is designed to map several bits to a direction vector.

Secret data can be extracted by comparing the angle between the direction of prediction error and direction vector, and the original model can be restored using the referenced set.

Experiment results show that compared with the existing data hiding method for encrypted 3D model, the proposed method has higher data hiding capacity, and the accuracy of data extraction have improved.

Moreover, the directly decrypted model has less distortion.