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Enhancing visual quality of spatial image steganography using SqueezeNet deep learning network

  • 1171: Real-Time 2D/ 3D Image Processing with Deep Learning
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Abstract

The aims of improving steganographic method are divided into two groups: the first is to make the hiding capacity as high as possible; the second is to make the visible distortion as low as possible. The higher the visual quality of the stego-image, the less suspicious it becomes, which can increase security. However, the distortion caused by embedding data into images is not predictable and typically image dependent. If the user has a database of possible cover images, finding a suitable cover image that can sustain high visual quality after embedding is challenging. Thus, an automatic cover selection method is needed. In this paper, the problem of visual quality of the stego-image is tackled as a classification problem, where a CNN-based classifier is employed to select images that can have high imperceptibility after the process of embedding. To achieve that, a CNN was trained to classify images into “High Quality” and “Low Quality”. The CNN was based on SqueezeNet architecture, and was trained in two scenarios; transfer learning and learning from scratch. The two classifiers were able to achieve very high classification accuracies of F1 = 0.926 and 0.904.

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References

  1. Al-Qershi OM, Khoo BE (2014) Controlling hiding capacity using image characteristics with a 2D-DE data hiding scheme. AEU Int J Electron Commun 68(4):346–350

    Article  Google Scholar 

  2. Bas P, Filler T, Pevný T (2011) Break Our Steganographic System the ins and outs of organizing BOSS BT—information hiding. In Proceedings of the 13th international conference on information hiding, pp 59–70

  3. Cruz N, Lobos-Tsunekawa K, Ruiz-del-Solar J (2018) Using convolutional neural networks in robots with limited computational resources: detecting NAO robots while playing Soccer BT—RoboCup 2017: Robot World Cup XXI, pp 19–30

  4. Cychnerski J, Brzeski A, Boguszewski A, Marmolowski M, Trojanowicz M (2017) Clothes detection and classification using convolutional neural networks. In 2017 22nd IEEE international conference on emerging technologies and factory automation (ETFA), pp 1–8

  5. El Abbadi NK (2013) Cover optimization for image in image steganography. Int J Comput Sci Issues 10(1):556–564

    Google Scholar 

  6. Evsutin O, Kokurina A, Meshcheryakov R (2018) Approach to the selection of the best cover image for information embedding in JPEG images based on the principles of the optimality. J Decis Syst 27(sup1):256–264

    Article  Google Scholar 

  7. Grībermans D, Jeršovs A, Rusakovs P (2016) Development of requirements specification for steganographic systems. Appl Comput Syst 20(1):40–48

    Article  Google Scholar 

  8. Han X, Zhong Y, Cao L, Zhang L (2017) Pre-trained AlexNet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification. Remote Sens 9(8):848

    Article  Google Scholar 

  9. Hajduk V, Levický D (2016) Cover selection steganography. In 2016 International symposium ELMAR, pp 205–208

  10. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  11. Hetherington J, Lessoway V, Gunka V, Abolmaesumi P, Rohling R (2017) SLIDE: automatic spine level identification system using a deep convolutional neural network. Int J Comput Assist Radiol Surg 12(7):1189–1198

    Article  Google Scholar 

  12. Holub V, Fridrich J, Denemark T (2014) Universal distortion function for steganography in an arbitrary domain. EURASIP J Inf Secur 2014:1–13

    Article  Google Scholar 

  13. Holub V, Fridrich J (2012) Designing steganographic distortion using directional filters. In IEEE international workshop on information forensics and security (WIFS), pp 234–239

  14. Hradiš M, Kotera J, Zemcık P, Šroubek F (2015) Convolutional neural networks for direct text deblurring. In Proceedings of BMVC, vol 10, p 2

  15. Huang W, Zhao X (2016) Novel cover selection criterion for spatial steganography using linear pixel prediction error. Sci China Inf Sci 59:1–3

    Google Scholar 

  16. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size, pp 1–13. arXiv preprint arXiv:1602.07360

  17. ImageNet. http://www.image-net.org

  18. Johnson NF, Jajodia S (1998) Exploring steganography: seeing the unseen. Computer (Long Beach Calif) 31(2):26–34

    Google Scholar 

  19. Kouider S, Chaumont M, Puech W (2012) Technical points about adaptive steganography by oracle (ASO). In Proceedings of the 20th European signal processing conference (EUSIPCO), pp 1703–1707

  20. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pp 1097–1105

  21. Lin G, Wu Q, Qiu L, Huang X (2018) Image super-resolution using a dilated convolutional neural network. Neurocomputing 275:1219–1230

    Article  Google Scholar 

  22. Ng H-W, Nguyen VD, Vonikakis V, Winkler S (2015) Deep learning for emotion recognition on small datasets using transfer learning. In Proceedings of the 2015 ACM on international conference on multimodal interaction, pp 443–449

  23. Ouyang W et al (2015) Deepid-net: deformable deep convolutional neural networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2403–2412

  24. Peng M, Wang C, Chen T, Liu G (2016) Nirfacenet: a convolutional neural network for near-infrared face identification. Information 7(4):61

    Article  Google Scholar 

  25. Quattoni A, Torralba A (2009) Recognizing indoor scenes. In: 2009 IEEE conference on computer vision and pattern recognition, pp 413–420. https://ieeexplore.ieee.org/document/5206537. Accessed 24 Aug 2021

  26. Sajedi H, Jamzad M (2010) Using contourlet transform and cover selection for secure steganography. Int J Inf Secur 9(5):337–352

    Article  Google Scholar 

  27. Sajedi H, Jamzad M (2010) Selecting a reliable steganography method. In Proceedings of the international conference on multimedia computing and information technology (MCIT), pp 69–72

  28. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In Proceedings of international conference on learning representations, ICLR,

  29. Subhedar MS, Mankar VH (2013) Performance evaluation of image steganography based on cover selection and contourlet transform. In 2013 International conference on cloud and ubiquitous computing and emerging technologies, pp 172–177

  30. Sun Y, Liu F (2010) Selecting cover for image steganography by correlation coefficient. In Proceedings of the second international workshop on education technology and computer science, vol 2, pp 159–162

  31. Szegedy C et al (2015) Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  32. Wu S, Liu Y, Zhong S, Liu Y (2015) What makes the stego image undetectable? In Proceedings of the 7th international conference on Internet multimedia computing and service, pp 47–52

  33. Xie M, Jean N, Burke M, Lobell D, Ermon S (2016) Transfer learning from deep features for remote sensing and poverty mapping. In Proceedings of the 13th AAAI conference on artificial intelligence, 

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Authors and Affiliations

  1. College of Business Informatics, University of Information Technology and Communications, Baghdad, Iraq

    Nagham Hamid & Bilal Ibrahim Bakri

  2. Department of Electrical Engineering, Anbar University, Ramadi, Anbar, Iraq

    Balasem Salem Sumait

  3. Department of Data Science and AI, Faculty of IT, Monash University, Melbourne, Australia

    Osamah Al-Qershi

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  1. Nagham Hamid
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  2. Balasem Salem Sumait
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  3. Bilal Ibrahim Bakri
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  4. Osamah Al-Qershi
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Correspondence to Osamah Al-Qershi.

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Hamid, N., Sumait, B.S., Bakri, B.I. et al. Enhancing visual quality of spatial image steganography using SqueezeNet deep learning network. Multimed Tools Appl 80, 36093–36109 (2021). https://doi.org/10.1007/s11042-021-11315-y

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  • DOI: https://doi.org/10.1007/s11042-021-11315-y

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