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An Improved Security 3D Watermarking Method Using Computational Integral Imaging Cryptosystem

An Improved Security 3D Watermarking Method Using Computational Integral Imaging Cryptosystem
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Author(s): Yiqun Liu (Xidian University & Engineering University of Chinese Armed Police Force, China), Xiaorui Wang (Xidian University, China), Jianqi Zhang (Xidian University, China), Minqing Zhang (Engineering University of Chinese Armed Police Force, China), Peng Luo (Engineering University of Chinese Armed Police Force, China) and Xu An Wang (Engineering University of Chinese Armed Police Force, China)
Copyright: 2018
Pages: 21
Source title: Digital Multimedia: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-3822-6.ch028

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Abstract

Robustness and security are difficult to be solved by conventional two-dimensional (2D) digital watermarking technology. In recent years, three-dimensional (3D) digital watermarking has become a new hotspot in optical information security. This paper presents a new improved security 3D digital watermarking method based on computational integrated imaging cryptosystem. Firstly, 3D digital watermarking is generated and encrypted by computational integral imaging cryptosystem that is implemented with smart pseudoscopic-to-orthoscopic conversion (SPOC) model. Secondly, discrete wavelet transform algorithm is applied to embed and extract the 3D digital watermarking. Finally, the extracted watermark is decrypted, and3D digital watermarking is displayed by integral imaging system. The feasibility and effectiveness of the proposed method is demonstrated by experiment. A primary implication of encrypted processing is that the majority of integral imaging cryptosystem will be encryption-in-the-loop applications, and the majority of system will improve the security and robustness of 3D digital watermarking. The new method is able to meet the requirements of robustness and security. Image quality and display quality achieve these criterions of the human visual model. The proposed method can be applied in the aspects of cloud computing and big data.

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