GUESS: Genetic Uses in Video Encryption with Secret Sharing

Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 703)

Abstract

Nowadays, video security systems are essential for supervision everywhere, for example video conference, WhatsApp, ATM, airport, railway station, and other crowded places. In multi-view video systems, various cameras are producing a huge amount of video content which makes it difficult for fast browsing and securing the information. Due to advancement in networking, digital cameras, and media, interactive sites, the importance of privacy and security is rapidly increasing. Hence, nowadays the security of digital videos become an emerging research area in the multimedia domain; especially when the communication happens over the Internet. Cryptography is an essential practice to protect the information in this digital world. Standard encryption techniques like AES/DES are not optimal and efficient in case of videos. Therefore, a technique is immediately required, which can provide the security to video content. In this paper, we address the video security-related issues and their solutions. An optimized version of the genetic algorithm is employed to solve the aforementioned issues through modeling the simplified version of genetic processes. It is used to generate a frame sequence such that the correlation between any two frames is minimized. The frame sequence determines the randomization in order of frames of a video. The proposed method is not only fast but also more accurate to enhance the efficiency of an encryption process.

Keywords

Video encryption Genetic algorithm Secret sharing 
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References

  1. 1.
    K., Krishan, et al. “Eratosthenes sieve based key-frame extraction technique for event summarization in videos.” MTAP (2017), pp. 1–22.Google Scholar
  2. 2.
    Kelahmetoglu, et al., Efficient utility of WhatsApp: from computer screen to the surgeon’s hand to determine maxillofacial traumas, JCS, 26, 4, (2015), pp. 1437.Google Scholar
  3. 3.
    G. A. Spanos, et al., Performance Study of a Selective Encryption Scheme for the Security of Networked Real-Time Video, ICCCN, (1995), pp. 2–10.Google Scholar
  4. 4.
    K., Kumar, et al., Equal Partition based Clustering approach for Event Summarization in Videos, SITIS’16, 2016, pp. 119–126.Google Scholar
  5. 5.
    K., Sridhar, et al., System method and article of manufacture with integrated video conferencing billing in a communication system architecture, U.S. Patent No. 5,867,494. (1999).Google Scholar
  6. 6.
    M., Sari, What Up with WhatsApp, TABJ, (2014), pp. 28.Google Scholar
  7. 7.
    A., Mohamed, et al., An overview of video encryption techniques, IJCTE, 2, 1, (2010), pp. 103–110.Google Scholar
  8. 8.
    W., Chung, et al., Fast encryption methods for audiovisual data confidentiality, ITISOP, 2001.Google Scholar
  9. 9.
    C., Shi, et al., A fast MPEG video encryption algorithm, ACM ICM’98.Google Scholar
  10. 10.
    M., Gough, Video conferencing over IP: Configure, secure, and troubleshoot, Elsevier, (2006).Google Scholar
  11. 11.
    A. Senior et al., Enabling video privacy through computer vision, IEEE Security & Privacy, 3, 3, (2005), pp. 50-57.Google Scholar
  12. 12.
    W. Zhang, S. S. Cheung and Minghua Chen, Hiding privacy information in video surveillance system, IEEE ICIP, (2005), pp. 868–871.Google Scholar
  13. 13.
    G.R. Blakley, Safeguarding Cryptographic Keys, Proc. Am. Federation of Information Processing Soc, 48, (1979), pp. 313-317.Google Scholar
  14. 14.
    A. Shamir, How to Share a Secret, Comm. ACM, 22, 11, (1979), pp. 612–613.Google Scholar
  15. 15.
    S. J., Gustavus, Symmetric and asymmetric encryption, ACM Computing Surveys (CSUR) 11, 4, (1979), pp. 305-330.Google Scholar
  16. 16.
    L., Wei, Using genetic algorithm for network intrusion detection, Proceedings of the United States Department of Energy Cyber Security Group, 1, (2004), pp. 1–8.Google Scholar
  17. 17.
    A. Kumar, N. Rajpal, Application of Genetic Algorithm in the Field of Steganography, Journal of Information Technology, 2, 1, (2004), pp. 12-15.Google Scholar
  18. 18.
    A. Kumar, et al., New Signal Security System for Multimedia Data Transmission Using Genetic Algorithms, NCC’05, IIT Kharagpur (2005) pp. 579–583.Google Scholar
  19. 19.
    A. Soni, et al., Key Gen. Using Genetic Algorithm for Image Encryption, (2013).Google Scholar
  20. 20.
    L., Qiao, et al., A new algorithm for MPEG video encryption, ICISST, 1997.Google Scholar
  21. 21.
    A. Tragha, et al., ICIGA: Improved Cryptography Inspired by Genetic Algorithms, ICHIT’06, (2006).Google Scholar
  22. 22.
    S. Wang, et al., A secure steganography method based on genetic algorithm, JIHMSP, 1, 1, (2010), pp. 28–35.Google Scholar
  23. 23.
    Hasso, et al., Steganography in Video Files, IJCSI, 13, 1, (2016), pp. 32.Google Scholar
  24. 24.
    K. Krishan, et al., Event BAGGING: A novel event summarization approach in multi-view surveillance videos, IEEE IESC’17, 2017.Google Scholar
  25. 25.
    K. Kumar, et al., “SOMES: An efficient SOM technique for Event Summarization in multi-view surveillance videos,” Springer ICACNI’17, 2017.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringNITSrinagarIndia

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