TRACK-S-IT: Multiobject Tracking-based Steganography for Securing IoMT Data

Authors

  • Sahar Magdy Department of Computer Engineering, Pharos University, Alexandria Governate 21648, Egypt
  • Sherin Youssef Department of Computer Engineering, Arab Academy for Science and Technology, Alexandria Governate 5422020, Egypt
  • Karma M. Fathalla Department of Computer Engineering, Arab Academy for Science and Technology, Alexandria Governate 5422020, Egypt

DOI:

https://doi.org/10.37934/araset.48.1.227239

Keywords:

Steganography, Security, IoMT, Deep learning, Tracking MOT, AES, Least significant bit

Abstract

Internet of Medical Things (IoMT) facilitates medical services including real-time diagnosis, remote patient monitoring, and real-time medicine prescriptions. IoMT incorporates Internet of Things in medical systems. However, IoMT devices are often built with no security in mind, which make them susceptible to various attacks, such as data theft, manipulation, and denial of service. Therefore, security and privacy are essential for the wider adoption and trust of IoMT. In this paper, a video tracking- based CryptoStegno model is proposed to secure private and medical records in an IoMT environment. Private information protection is made possible through crypto-steganography. An added layer of protection is guaranteed through video tracking technology, where data is embedded at multiple tracked objects. In addition, video steganography handles the issue of embedding capacity via utilizing multiple frames. Thus, this paper proposes a novel CryptoStegno model for embedding medical and private data based on video Steganography. Also, AES cryptography is used to encrypt the data before the embedding process to provide a high level of security. Hence, the proposed approach provides robustness and security to the data. On a variety of video sequences, the proposed scheme is examined using different metrics to ensure the robustness of the model such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity (SSIM) Index, and Bit Error Rate (BER). In terms of PSNR, an improvement of around 2% of was achieved compared to the state of the art, while 5% and 9% improvements were achieved in terms of SSIM and RMSE, respectively.

Author Biographies

Sahar Magdy, Department of Computer Engineering, Pharos University, Alexandria Governate 21648, Egypt

sahar.magdy@pua.edu.eg

Sherin Youssef, Department of Computer Engineering, Arab Academy for Science and Technology, Alexandria Governate 5422020, Egypt

sherin@aast.edu

Karma M. Fathalla, Department of Computer Engineering, Arab Academy for Science and Technology, Alexandria Governate 5422020, Egypt

karma.fathalla@aast.edu

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Published

2024-07-10

How to Cite

Sahar Magdy, Sherin Youssef, & Karma M. Fathalla. (2024). TRACK-S-IT: Multiobject Tracking-based Steganography for Securing IoMT Data. Journal of Advanced Research in Applied Sciences and Engineering Technology, 48(1), 227–239. https://doi.org/10.37934/araset.48.1.227239

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Section

Articles