3D Point Cloud Encryption Through Chaotic Mapping

Jin, Xin; Zhaoxing, Wu; Song, Chaoyun; Zhang, Chunwei

doi:10.1007/978-3-319-48890-5_12

Cited by 20 publications

(16 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The point cloud is a new kind of visual data representing a 3D structure of an object. Some extended chaotic mapping algorithms [ 29 , 30 ] have been used to encrypt the point cloud. Furthermore, a series of random permutations and rotations have been employed to encrypt the point cloud by deforming the geometry [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

Nowadays, our lives have benefited from various vision-based applications, such as video surveillance, human identification and aided driving. Unauthorized access to the vision-related data greatly threatens users’ privacy, and many encryption schemes have been proposed to secure images and videos in those conventional scenarios. Neuromorphic vision sensor (NVS) is a brand new kind of bio-inspired sensor that can generate a stream of impulse-like events rather than synchronized image frames, which reduces the sensor’s latency and broadens the applications in surveillance and identification. However, the privacy issue related to NVS remains a significant challenge. For example, some image reconstruction and human identification approaches may expose privacy-related information from NVS events. This work is the first to investigate the privacy of NVS. We firstly analyze the possible security attacks to NVS, including grayscale image reconstruction and privacy-related classification. We then propose a dedicated encryption framework for NVS, which incorporates a 2D chaotic mapping to scramble the positions of events and flip their polarities. In addition, an updating score has been designed for controlling the frequency of execution, which supports efficient encryption on different platforms. Finally, extensive experiments have demonstrated that the proposed encryption framework can effectively protect NVS events against grayscale image reconstruction and human identification, and meanwhile, achieve high efficiency on various platforms including resource-constrained devices.

show abstract

Section: Related Workmentioning

confidence: 99%

Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Zhao

Mou

2020

IEEE Access

View full text Add to dashboard Cite

In this paper, a novel 3D image file encryption algorithm based on a new discrete chaotic system is presented. Chaotic characteristics of the novel chaotic system are analyzed by phase diagram, Lyapunov exponent, bifurcation diagram. Based on the analysis results, an encryption scheme is designed for 3D image file. The hash value which is calculated from the SHA-256 hash function is used to change the initial condition of the discrete system firstly. And then, the chaotic sequences are used to scramble and spread coordinate value for 3D image file by Arnold matrix and DNA diffusion algorithm. The security analysis shows that the proposed 3D image file encryption algorithm possesses higher security features to preserve the subject and resist conventional attack. The results of this paper give a different application scheme of the 3D image encryption algorithm.

show abstract

“…The surface models include point clouds, meshes and textured models. Current methods in this direction only consider the solid models [3], the point clouds models [9,8], the meshes [4] and the textures [10]. This paper focus on encryption of the most complicated 3D surface models.…”

Section: Introductionmentioning

confidence: 99%

3D textured model encryption via 3D Lu chaotic mapping

Jin

Zhu

Xiao

et al. 2017

Sci. China Inf. Sci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

3D Point Cloud Encryption Through Chaotic Mapping

Cited by 20 publications

References 11 publications

Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation

Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation

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

3D textured model encryption via 3D Lu chaotic mapping

Contact Info

Product

Resources

About