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A secure social multimedia sharing scheme in the TSHWT_SVD domain based on neural network

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Abstract

The rapid development of mobile social networks and wireless communication technology has made image/video sharing easier and more efficient. However, the convenience of social multimedia sharing can also cause serious problems such as privacy disclosure because of illegal use of shared contents, and the existing content sharing scheme cannot meet the privacy protection requirements of users in social networks. Therefore, secure multimedia sharing and privacy protection have become critical and urgent in multimedia social networks. For protecting multimedia sharing in multimedia social networks, a novel joint fingerprinting and encryption (JFE) scheme is proposed. Both fingerprint embedding and encryption are performed in the tree structure haar wavelet transform and singular value decomposition (TSHWT_SVD) domain based on chaotic neural network. First, the social image is decomposed based on the fingerprint code structure by the TSHWT. Then, perform SVD computing for selective subbands for parallel piecewise fingerprint segments embedding. In the end, the fingerprinted coefficient stream is encrypted via block permutation and SVD diffusion. It is worth mentioning that most of the existing image security algorithms encrypt and embed a watermark in the spatial domain, which cannot meet the requirements of the era of multimedia social networks due to lack of scalability. The proposed method, to the best of our knowledge, is the first scalable JFE method for fingerprinting and encryption in the TSHWT_SVD domain. The use of fingerprinting along with encryption in the TSHWT_SVD domain can provide scalable double-layer protection for secure social multimedia sharing. When compared with existing image security algorithms, the scalable selective encryption method greatly improves encryption efficiency. Moreover, experimental results and contrastive analyses show that the proposed JFE scheme has high security, fast speed and can resist various attacks.

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Acknowledgments

This paper is supported by Natural Science Foundation of China (No.61502154, No.61972136, No.61370092), NSF of Chinese Hubei Provinc (No.2015CFB236), Natural Science Fundation of Hubei Provincial Department of Education (No.D20142703), Philosophy and Social Science Research Project of Hubei Provincial Department of Education (No.21Q213).

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Authors and Affiliations

  1. School of Information and Communication Engineering, Hubei University of Economics, Wuhan, 430205, Hubei, China

    Conghuan Ye, Shenglong Tan, Zheng Wang, Li Shi & Jun Wang

  2. School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, Hubei, China

    Conghuan Ye

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Shenglong Tan, Zheng Wang, Li Shi and Jun Wang are contributed equally to this work.

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Ye, C., Tan, S., Wang, Z. et al. A secure social multimedia sharing scheme in the TSHWT_SVD domain based on neural network. Multimed Tools Appl 82, 15395–15414 (2023). https://doi.org/10.1007/s11042-022-13953-2

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