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THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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Research Trends in Steganography Based on Artificial Intelligence

인공지능 기반 스테가노그래피 생성 기술 최신 연구 동향

  • 김현지 (한성대학교 IT융합공학과) ;
  • 임세진 (한성대학교 IT융합공학과) ;
  • 김덕영 (한성대학교 IT융합공학과) ;
  • 윤세영 (한성대학교 IT융합공학과) ;
  • 서화정 (한성대학교 융합보안학과)
  • Received : 2023.04.12
  • Accepted : 2023.05.24
  • Published : 2023.05.31
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Abstract

Steganography is a technology capable of protecting data by hiding the existence of data. Recently, with the development of deep learning technology, deep learning-based steganography are being developed. Deep learning can learn by analyzing high-dimensional features of data, so it can improve the performance and quality of steganography. In this paper, we investigated the research trend of image steganography based on deep learning.

스테가노그래피는 데이터의 존재 자체를 은닉하여 데이터를 보호하는 기술이다. 최근에는 인공지능 기술이 발달함에 따라 딥러닝 기반의 스테가노그래피 기법들이 개발되고 있다. 딥러닝 기술은 데이터에 대한 고차원의 특징을 분석하여 학습할 수 있으므로 스테가노그래피의 성능과 품질을 개선시킬 수 있다. 본 논문에서는 이미지데이터에 대한 딥러닝 기반의 스테가노그래피 기술의 최신 연구 동향에 대해 살펴보도록 한다.

Keywords

Acknowledgement

This research was financially supported by Hansung University.

References

  1. DJEBBAR, Fatiha, "Securing IoT Data Using Steganography: A Practical Implementation Approach," Electronics, 10.21: 2707, 2021.
  2. KHARI, Manju, et al., "Securing data in Internet of Things (IoT) using cryptography and steganography techniques," IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 50, Issue 1, PP. 73-80, Jan. 2020. https://doi.org/10.1109/TSMC.2019.2903785
  3. Akhtar, Nadeem, Pragati Johri, and Shahbaaz Khan. "Enhancing the security and quality of LSB based image steganography," 2013 5th International Conference and Computational Intelligence and Communication Networks, Mathura, India, Sep. 2013.
  4. Jian, Chua Teck, et al., "Audio steganography with embedded text," IOP Conference Series: Materials Science and Engineering, Vol. 226. No. 1, Aug. 2017.
  5. Kim, Jaeyoung, et al., "A statistical approach for improving the embedding capacity of block matching based image steganography," Journal of Broadcast Engineering, Vol. 22, Issue 5, pp. 643-651, 2017.
  6. Kavitha, Kavita Kadam, Ashwini Koshti, and Priya Dunghav, "Steganography using least significant bit algorithm," International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue 3, pp. 2248-9622, May-Jun 2012.
  7. Gupta, Shailender, Ankur Goyal, and Bharat Bhushan, "Information hiding using least significant bit steganography and cryptography," International Journal of Modern Education and Computer Science, 4.6: 27, Jun. 2012.
  8. Wu, Da-Chun, and Wen-Hsiang Tsai, "A steganographic method for images by pixel-value differencing," Pattern recognition letters, Vol. 24, Issues 9-10, pp. 1613-1626, Jun. 2003. https://doi.org/10.1016/S0167-8655(02)00402-6
  9. Nurdiyanto, Heri, et al., "Enhanced pixel value differencing steganography with government standard algorithm," 2017 3rd International Conference on Science in Information Technology (ICSITech), Bandung, Indonesia, Oct. 2017.
  10. H.-C. Wu, et al., "Image steganographic scheme based on pixel-value differencing and LSB replacement methods," IEE Proceedings-Vision, Image and Signal Processing, Vol. 152, Issues 5, pp. 611-615, Oct. 2005. https://doi.org/10.1049/ip-vis:20059022
  11. C.-H. Yang, et al., "Capacity-raising steganography using multi-pixel differencing and pixel-value shifting operations," Fundamenta Informaticae, vol. 98, no. 2-3, pp. 321- 336, 2010. https://doi.org/10.3233/FI-2010-229
  12. V. Holub and J. Fridrich, "Designing steganographic distortion using directional filters," 2012 IEEE International Workshop on Information Forensics and Security(WIFS), Vol.2, pp. 234-239, 2012.
  13. Holub, V., Fridrich, J., "Digital image steganography using universal distortion," Proceedings of the First ACM Workshop on Information Hiding and Multimedia Security, pp. 59-68, Montpellier, France, Jun. 2013.
  14. Nam, Soo-Tai and Chan-Yong Jin, "Rebuilding of Image Compression Algorithm Based on the DCT (discrete cosine transform)," Journal of the Korea Institute of Information and Communication Engineering, vol. 23, no. 1, pp. 84-89, 2019.
  15. Sahar A. El_Rahman, "A comparative analysis of image steganography based on DCT algorithm and steganography tool to hide nuclear reactors confidential information," Computers &Electrical Engineering, Vol. 70, pp. 380-399, Aug. 2018. https://doi.org/10.1016/j.compeleceng.2016.09.001
  16. Dr. Ekta Walia, Payal Jain, Navdeep, "An Analysis of LSB & DCT based Steganography," Global Journal of Computer Science and Technology, Vol. 10, Issue 1, pp.4-8, Apr. 2010.
  17. A.M.Raid, W.M.Khedr, M. A. El-dosuky and Wesam Ahmed, "Jpeg Image Compression Using Discrete Cosine Transform - A Survey," International Journal of Computer Science & Engineering Survey(IJCSES), Vol. 5, No. 2, Apr. 2014.
  18. R. Reininger and J. Gibson, "Distributions of the Two-Dimensional DCT Coefficients for Images," IEEE Transactions on Communications, Vol. 31, Issue 6, pp. 835-839, Jun. 1983. https://doi.org/10.1109/TCOM.1983.1095893
  19. Ken Cabeen, Peter Gent, "Image Compression and the Discrete Cosine Transform,"
  20. Baluja, Shumeet, "Hiding images in plain sight: Deep steganography," Advances in neural information processing systems 30, 2017.
  21. Albawi, Saad, Tareq Abed Mohammed and Saad Al-Zawi, "Understanding of a convolutional neural network," 2017 international conference on engineering and technology (ICET). pp. 1-6, Antalya, Turkey, Aug. 2017.
  22. Volkhonskiy, Denis, Ivan Nazarov and Evgeny Burnaev, "Steganographic generative adversarial networks," Twelfth international conference on machine vision (ICMV 2019), Vol. 11433, pp. 991-1005, Jan. 2020.
  23. Goodfellow, Ian, et al., "Generative adversarial networks," Communications of the ACM, Vol. 63, no. 11, pp. 139-144, Oct. 2020. https://doi.org/10.1145/3422622
  24. Wu, Pin, Yang Yang and Xiaoqiang Li, "Stegnet: Mega image steganography capacity with deep convolutional network," Future Internet, Vol. 10, Issue 6, pp. 54, Jun. 2018.
  25. Srivastava, et al., "Highway networks," arXiv:1505.00387, Nov. 2015.
  26. He, Kaiming, et al., "Deep residual learning for image recognition," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, Las Vegas, NV, USA, Jun. 2016.
  27. Szegedy, Christian, et al., "Rethinking the inception architecture for computer vision," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818-2826, Las Vegas, NV, USA, Jun. 2016.
  28. Kingma, Diederik P. and Max Welling, "An introduction to variational autoencoders," Foundations and Trends® in Machine Learning, Vol. 12, No. 4, pp. 307-392, Nov. 2019. https://doi.org/10.1561/2200000056
  29. Baluja, Shumeet, "Hiding images within images," IEEE transactions on pattern analysis and machine intelligence, Vol. 42, Issue 7, pp. 1685-1697, Feb. 2019. https://doi.org/10.1109/TPAMI.2019.2901877
  30. Shi, Haichao, et al., "SSGAN: Secure steganography based on generative adversarial networks," Advances in Multimedia Information Processing-PCM 2017, pp. 534-544, Harbin, China, September 28-29, 2017.
  31. WANG, Yaojie, Ke NIU and Xiaoyuan YANG, "Information hiding scheme based on generative adversarial network," Journal of Computer Applications, Vol. 38, Issue 10, pp. 2923-2928, 2018.
  32. Zhang, Kevin Alex, et al., "SteganoGAN: High capacity image steganography with GANs," arXiv:1901.03892, 2019.