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인공지능 기반 스테가노그래피 생성 기술 최신 연구 동향

Research Trends in Steganography Based on Artificial Intelligence

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키워드

주제분류컴퓨터학

초록

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 이미지 데이터에 대한 딥러닝 기반의 스테가노그래피 기술의 최신 연구 동향에 대해 살펴보도록 한다.

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.

참고문헌 (32)

  1. DJEBBAR, Fatiha, "Securing IoT Data Using Steganography : A Practical Implementation Approach", Electronics, 10(21), pp.2707~ (2021)
  2. KHARI, Manju, "Securing data in Internet of Things (IoT) using cryptography and steganography techniques", IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(1), pp.73~80 (2020)
  3. Akhtar, Nadeem, "Enhancing the security and quality of LSB based image steganography", 2013 5th International Conference and Computational Intelligence and Communication Networks (2013)
  4. Jian, Chua Teck, "Audio steganography with embedded text", IOP Conference Series:Materials Science and Engineering, 226(1) (2017)
  5. 김재영, "블록 매칭 기반 영상 스테가노그래피의 삽입 용량 개선을 위한 통계적 접근 방법", 방송공학회 논문지, 22(5), pp.643~651 (2017), 한국방송∙미디어공학회[DOI]
  6. Kavitha, Kavita Kadam, "Steganography using least significant bit algorithm", International Journal of Engineering Research and Applications (IJERA), 2(3), pp.2248~9622 (2012)
  7. Gupta, Shailender, "Information hiding using least significant bit steganography and cryptography", International Journal of Modern Education and Computer Science, 4(6), pp.27~ (2012)
  8. Wu, Da-Chun, "Asteganographic method for images by pixel-value differencing", Pattern recognition letters, 24(9–10), pp.1613~1626 (2003)
  9. Nurdiyanto, Heri, "Enhanced pixel value differencing steganography with government standard algorithm", 2017 3rd International Conference on Science in Information Technology (ICSITech) (2017)
  10. H.-C. Wu, "Image steganographic scheme based on pixel-value differencing and LSB replacement methods", IEE Proceedings-Vision, Image and Signal Processing, 152(5), pp.611~615 (2005)
  11. C.-H. Yang, "Capacity-raising steganography using multi-pixel differencing and pixel-value shifting operations", Fundamenta Informaticae, 98(2-3), pp.321~336 (2010)
  12. V. Holub, "Designing steganographic distortion using directional filters", 2012 IEEE International Workshop on Information Forensics and Security(WIFS), 2, pp.234~239 (2012)
  13. Holub, V., "Digital image steganography using universal distortion", Proceedings of the First ACM Workshop on Information Hiding and Multimedia Security, pp.59~68 (2013)
  14. 남수태, "이산코사인변환 기반 이미지 압축 알고리즘에 관한 재구성", 한국정보통신학회논문지, 23(1), pp.84~89 (2019), 한국정보통신학회[DOI]
  15. Sahar A. El_Rahman, "A comparative analysis of image steganography based on DCTalgorithm and steganography tool to hide nuclear reactors confidential information", Computers &Electrical Engineering, 70, pp.380~399 (2018)
  16. Dr. Ekta Walia, "Analysis of LSB & DCT based Steganography", Global Journal of Computer Science and Technology, 10(1), pp.4~8 (2010)
  17. A.M.Raid, "Jpeg Image Compression Using Discrete Cosine Transform - A Survey", International Journal of Computer Science &Engineering Survey(IJCSES), 5(2) (2014)
  18. R. Reininger, "Distributions of the Two-Dimensional DCT Coefficients for Images", IEEE Transactions on Communications, 31(6), pp.835~839 (1983)
  19. Ken Cabeen, "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, "Understanding of a convolutional neural network", 2017 international conference on engineering and technology (ICET), pp.1~6 (2017)
  22. Volkhonskiy, Denis, "Steganographic generative adversarial networks", Twelfth international conference on machine vision(ICMV 2019), 11433, pp.991~1005 (2020)
  23. Goodfellow, Ian, "Generative adversarial networks", Communications of the ACM, 63(11), pp.139~144 (2020)
  24. Wu, Pin, "Stegnet: Mega image steganography capacity with deep convolutional network", Future Internet, 10(6), pp.54~ (2018)
  25. Srivastava, "Highway networks", ~ (2015)
  26. He, Kaiming, "Deep residual learning for image recognition", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770~778 (2016)
  27. Szegedy, Christian, "Rethinking the inception architecture for computer vision", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2818~2826 (2016)
  28. Kingma, Diederik P., "An introduction to variational autoencoders", Foundations and Trends® in Machine Learning, 12(4), pp.307~392 (2019)
  29. Baluja, Shumeet, "Hiding images within images", IEEE transactions on pattern analysis and machine intelligence, 42(7), pp.1685~1697 (2019)
  30. Shi, Haichao, "SSGAN : Secure steganography based on generative adversarial networks", Advances in Multimedia Information Processing–PCM 2017, pp.534~544 (2017)
  31. WANG, Yaojie, "Information hiding scheme based on generative adversarial network", Journal of Computer Applications, 38(10), pp.2923~2928 (2018)
  32. Zhang, Kevin Alex, "SteganoGAN: High capacity image steganography with GANs" (2019)

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