location：Home > 2021 Vol.4 Mar.No.1 > Optical information hiding method based on machine learning theory

2021 Vol.4 Mar.No.1

  • Title: Optical information hiding method based on machine learning theory
  • Name: GautamSrivastava
  • Company: Department of Mathematics and Computer Science, Brandon University, Canada, POSTCODE
  • Abstract:

    Aiming at the problems of strong sensitivity and poor security in traditional optical information hiding methods, a virtual machine energy-saving migration distribution optical information hiding method based on machine learning theory is proposed. Construct a data leakage suppression and privacy protection coding model for virtual machine energy-saving migration distribution optical information encryption, and use the master key merge storage control method to implement the timestamp authentication code and master key authentication parameter search in the process of virtual machine energy-saving migration distribution optical information encryption. Establish a protection control algorithm for virtual machine energy-saving migration distribution optical information encryption, introduce compressed sensor sparse distribution mapping as a guide operator, carry out the decryption key design in the process of virtual machine energy-saving migration distribution optical information encryption, using machine learning and self The adaptive key leakage resistance mechanism realizes the energy-saving migration and distribution of virtual machines, optical information encryption and information hidden transmission. The simulation results show that using this method for virtual machine energy-saving migration distribution optical information encryption and information hiding transmission has good random scrambling, and the output of information hiding has strong stability, which improves the optical information protection and anti-attack capabilities.

     

  • Keyword: machine learning; Optical information; Hide; Master key
  • DOI: 10.12250/jpciams2021090115
  • Citation form: GautamSrivastava.Optical information hiding method based on machine learning theory[J]. Computer Informatization and Mechanical System,2021,Vol.4,pp.12-16
Reference:

  1. Yu Tao, Yang DongYu, Ma Rui, et al.Enhanced-visual-cryptography-based optical information hiding system[J].Acta Physica Sinica,2020,69(14):96-103.

  2. WANG Junxiang, MAO Ningxiong, ZHAO Yi, et al.Large Color Image Based High-Performance Reversible Data Hiding Hcheme for Various Capacities[J].Journal of Applied Sciences,2019,37(05):744-760.

  3. QU M, TANG J, SHANG J, et al. An attention-based collaboration framework for multi-view network representation learning[C]//Proceedings of the 2017 ACM Conference on Information and Knowledge Management. New York:ACM, 2017:1767-1776.

  4. SUN Y,WANG S,HSIEH T Y,et al. MEGAN:a generative adversarial network for multi-view network embedding[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence. San Francisco:Morgan Kaufmann,2019:3527-3533.

  5. WAN Yuzhang，ZHANG Xiaoming，XIONG Jijun, et al.Low Power Consumption High Precision Positioning Module Design for Wide Area Wireless Sensor Net Nodes Based on Post-RTK[J].Journal of Sichuan Ordnance,2018,39(12):212-217.

  6. Yao Fuguang, Zhong Xianxin, Zhou Jingchao.Granular computing:a new method of intelligent modeling for big data fusion[J].Journal of Nanjing University of Science and Technology,2018,42(04):503-510.

  7. WU Lei, YUAN Peng, DING Weilong.Research on Data Synchronization Protocol between Intelligent Home Gateway and Cloud Server[J].Computer Technology and Development,2018,28(09):151-155.

  8. QIN Shiyue, ZHOU Fucai, LIU Lu.Searchable Encryption Scheme of Genomic Data Based on Suffix Tree[J].
    Journal of Northeastern University(Natural Science),2019,40(04):461-466.

  9. EVANGELIO R H, PATZOLD M, and KELLER I. Adaptively splitted GMM with feedback improvement for the task of background subtraction[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(5): 863-874.

  10. NIU Shufen, YANG Xiyan, LI Zhenbin，et al. Hybrid Signcryption Scheme Based on Heterogeneous System[J]. Computer Engineering and Applications, 2019, 55(3): 61-67.     

  11. ZHONG Yanru, LIU Huayi, SUN Xiyan,et al.2D Chebyshev-Sine map for image encryption[J].
    Journal of Zhejiang University(Science Edition),2019(02):131-141+160.

  12. CHEN Xiaodong, DI Xiaoqiang, LI Jinqing. Optical image encryption algorithm based on multi-chaosand fractional Fourier[J].Journal of Nanjing University(Natural Science),2019(02):251-263.

  13. QIN Jing, AN Wen, JI Changqing, et al. Review of privacy protection mechanisms in wireless body area network[J]. Journal of Computer Applications, 2021, 41(4): 970-975.

  14. HE D, ZEADALLY S, KUMAR N, et al. Anonymous authentication for wireless body area networks with provable security[J]. IEEE Systems Journal,2017,11(4):2590-2601.

  15. YAN T,LU Y,ZHANG N. Privacy disclosure from wearable devices[C]//Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing. New York:ACM,2015:13-18.

  16. PENG Hui, CHEN Hong, ZHANG XiaoYing, et al. Location Privacy Preservation in Wireless Sensor Networks[J]. Journal of Software,2015,26(3):617-639.

  17. MURILLO-ESCOBAR M A,CARDOZA-AVENDAÑO L,LÓPEZGUTIÉRREZ R M, et al. A double chaotic layer encryption algorithm for clinical signals in telemedicine[J]. Journal of Medical Systems,2017,41(4):No. 59.

 

 

 

Tsuruta Institute of Medical Information Technology
Address:[502,5-47-6], Tsuyama, Tsukuba, Saitama, Japan TEL:008148-28809 fax:008148-28808 Japan,Email:jpciams@hotmail.com,2019-09-16