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Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression

Author

Listed:
  • Urmila Pilania

    (Department of CST, Manav Rachna University, Faridabad 121004, India)

  • Rohit Tanwar

    (School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India)

  • Mazdak Zamani

    (Department of Computer Science, New York University, 251 Mercer, New York, NY 10012, USA)

  • Azizah Abdul Manaf

    (Department of Internet Engineering and Computer Science, LKCFES, Universiti Tuanku Abdul Rahman, Kampar 31900, Malaysia)

Abstract

In today’s world of computers everyone is communicating their personal information through the web. So, the security of personal information is the main concern from the research point of view. Steganography can be used for the security purpose of personal information. Storing and forwarding of embedded personal information specifically in public places is gaining more attention day by day. In this research work, the Integer Wavelet Transform technique along with JPEG (Joint Photograph Expert Group) compression is proposed to overcome some of the issues associated with steganography techniques. Video cover files and JPEG compression improve concealing capacity because of their intrinsic properties. Integer Wavelet Transform is used to improve the imperceptibility and robustness of the proposed technique. The Imperceptibility of the proposed work is analyzed through evaluation parameters such as PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), SSIM (Structure Similarity Metric), and CC (Correlation Coefficient). Robustness is validated through some image processing attacks. Complexity is calculated in terms of concealing and retrieval time along with the amount of secret information hidden.

Suggested Citation

  • Urmila Pilania & Rohit Tanwar & Mazdak Zamani & Azizah Abdul Manaf, 2022. "Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression," Future Internet, MDPI, vol. 14(9), pages 1-16, August.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:9:p:254-:d:897695
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    References listed on IDEAS

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    1. Taher M. Ghazal & Mohammad Kamrul Hasan & Muhammad Turki Alshurideh & Haitham M. Alzoubi & Munir Ahmad & Syed Shehryar Akbar & Barween Al Kurdi & Iman A. Akour, 2021. "IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review," Future Internet, MDPI, vol. 13(8), pages 1-19, August.
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    Cited by:

    1. Jui-Chuan Liu & Heng-Xiao Chi & Ching-Chun Chang & Chin-Chen Chang, 2024. "An Innovative Information Hiding Scheme Based on Block-Wise Pixel Reordering," Future Internet, MDPI, vol. 16(1), pages 1-20, January.

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