3D textured model encryption via 3D Lu chaotic mapping

Abstract

In the coming Virtual/Augmented Reality (VR/AR) era, 3D contents will be popularized just as images and videos today. The security and privacy of these 3D contents should be taken into consideration. 3D contents contain surface models and solid models. The surface models include point clouds, meshes and textured models. Previous work mainly focus on encryption of solid models, point clouds and meshes. This work focuses on the most complicated 3D textured model. We propose a 3D Lu chaotic mapping based encryption method of 3D textured model. We encrypt the vertexes, the polygons and the textures of 3D models separately using the 3D Lu chaotic mapping. Then the encrypted vertices, edges and texture maps are composited together to form the final encrypted 3D textured model. The experimental results reveal that our method can encrypt and decrypt 3D textured models correctly. In addition, our method can resistant several attacks such as brute-force attack and statistic attack.

A 3D Image Encryption Algorithm Based on the Chaotic System and the Image Segmentation

“…Although, like another side of the coin, the 3D model file is holding the possibility of unauthorized access and it has a series of intellectual property issues. Therefore, the 3D model file protection has become a great deal of increasing interesting for information security research [7], [8]. Unlike the traditional text message, the 3D model file can be considered as a special image in the computeraided design software.…”

“…It cannot longer meet the encryption requirements. Ref [7], [8], [33], [34] using the chaotic system to encrypted the vertex information of 3D object. But these algorithms have drawbacks.…”

“…Firstly, the key space of Ref [33], [34] is small and the performance of resist brute force attack is weak. Secondly, the dynamic behavior of chaotic system in Ref [8], [33] is not complicated. Besides, the encryption algorithms of Ref [7], [8], [34] only have the diffusion part and no scramble operation.…”

Multi-view point cloud registration with adaptive convergence threshold and its application in 3D model retrieval

“…The adaptive convergence threshold proposed in this paper will reduce the manual participation, thus automate the registration algorithm. The reconstructed 3D models can be applied to the applications of 3D model retrieval, 3D textured model encryption [5], 3D point cloud encryption [6], etc.…”

Joint horizontal and vertical deep learning feature for vehicle re-identification

“…Before recognition, images are usually represented by features (e.g., texture [1], sparse coding [2], deep learning [3]) to resist some adverse factors (e.g., illumination changes, deformations, noise). In re-identification field, the view-point variation is a key challenge.…”