scholarly journals A Novel Reversible Data Hiding Method for 3D Model in Homomorphic Encryption Domain

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1090
Author(s):  
Ting Luo ◽  
Li Li ◽  
Shanqin Zhang ◽  
Shenxian Wang ◽  
Wei Gu
Keyword(s):  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
3D Model ◽  
Homomorphic Encryption ◽  
Cloud Service ◽  
3D Models ◽  
Embedding Capacity ◽  
Secret Data ◽  
Prediction Error Expansion

Reversible data hiding in the encrypted domain (RDH-ED) is a technique that protects the privacy of multimedia in the cloud service. In order to manage three-dimensional (3D) models, a novel RDH-ED based on prediction error expansion (PEE) is proposed. First, the homomorphic Paillier cryptosystem is utilized to encrypt the 3D model for transmission to the cloud. In the data hiding, a greedy algorithm is employed to classify vertices of 3D models into reference and embedded sets in order to increase the embedding capacity. The prediction value of the embedded vertex is computed by using the reference vertex, and then the module length of the prediction error is expanded to embed data. In the receiving side, the data extraction is symmetric to the data embedding, and the range of the module length is compared to extract the secret data. Meanwhile, the original 3D model can be recovered with the help of the reference vertex. The experimental results show that the proposed method can achieve greater embedding capacity compared with the existing RDH-ED methods.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Li Li ◽  
Shengxian Wang ◽  
Ting Luo ◽  
Ching-Chun Chang ◽  
Qili Zhou ◽  
...  
Keyword(s):  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
3D Model ◽  
Data Extraction ◽  
Direction Vector ◽  
Secret Data ◽  
Model Based ◽  
Prediction Error Expansion ◽  
Error Expansion

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.

scholarly journals A Location Map Reduced Reversible Data Hiding Method Using Prediction Error Modification

2012 ◽  
Vol 6-7 ◽  
pp. 428-433
Author(s):  
Yan Wei Li ◽  
Mei Chen Wu ◽  
Tung Shou Chen ◽  
Wien Hong
Keyword(s):  
Image Quality ◽  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Experimental Results ◽  
Cover Image ◽  
Prediction Errors ◽  
Secret Data ◽  
Location Map ◽  
Comparable Image Quality

We propose a reversible data hiding technique to improve Hong and Chen’s (2010) method. Hong and Chen divide the cover image into pixel group, and use reference pixels to predict other pixel values. Data are then embedded by modifying the prediction errors. However, when solving the overflow and underflow problems, they employ a location map to record the position of saturated pixels, and these pixels will not be used to carry data. In their method, if the image has a plenty of saturated pixels, the payload is decreased significantly because a lot of saturated pixels will not joint the embedment. We improve Hong and Chen’s method such that the saturated pixels can be used to carry data. The positions of these saturated pixels are then recorded in a location map, and the location map is embedded together with the secret data. The experimental results illustrate that the proposed method has better payload, will providing a comparable image quality.

scholarly journals Taylor expansion based stereo image reversible data hiding

2020 ◽  
Author(s):  
Xinyang Ying ◽  
Guobing Zhou
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Taylor Expansion ◽  
Stereo Image ◽  
Embedding Capacity ◽  
Stereo Images ◽  
Secret Data ◽  
Stego Image ◽  
Multi Level

Abstract The reversible data hiding allows original image to be completely recovered from the stego image when the secret data has been extracted, it is has drawn a lot of attentions from researchers. In this paper, a novel Taylor Expansion (TE) based stereo image reversible data hiding method is presented. Since the prediction accuracy is essential to the data hiding performance, a novel TE based predictor using correlations of two views of the stereo image is proposed. TE can fully exploit strong relationships between matched pixels in the stereo image so that the accuracy of the prediction can be improved. Then, histogram shifting is utilized to embed data to decrease distortion of stereo images, and multi-level hiding can increase embedding capacity. Experimental results show that the proposed method is superior to some existing data hiding methods considering embedding capacity and the quality of the stego stereo images.

scholarly journals Reversible Data Hiding with Pixel Prediction and Additive Homomorphism for Encrypted Image

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Chunqiang Yu ◽  
Xianquan Zhang ◽  
Zhenjun Tang ◽  
Yan Chen ◽  
Jingyu Huang
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Data Extraction ◽  
Visual Quality ◽  
Input Image ◽  
Embedding Capacity ◽  
Secret Data ◽  
Encrypted Image ◽  
High Embedding Capacity ◽  
Additive Homomorphism

Data hiding in encrypted image is a recent popular topic of data security. In this paper, we propose a reversible data hiding algorithm with pixel prediction and additive homomorphism for encrypted image. Specifically, the proposed algorithm applies pixel prediction to the input image for generating a cover image for data embedding, referred to as the preprocessed image. The preprocessed image is then encrypted by additive homomorphism. Secret data is finally embedded into the encrypted image via modular 256 addition. During secret data extraction and image recovery, addition homomorphism and pixel prediction are jointly used. Experimental results demonstrate that the proposed algorithm can accurately recover original image and reach high embedding capacity and good visual quality. Comparisons show that the proposed algorithm outperforms some recent algorithms in embedding capacity and visual quality.

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