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A deep learning-based steganography method for high dynamic range images

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Abstract

High dynamic range (HDR) images have recently drawn much attention in multimedia community. In this paper, we proposed an HDR image steganography method based on deep learning, which is for HDR images with OpenEXR format. To the best of our knowledge, this is the first steganography method that applies deep learning to HDR image steganography, and the first steganography method that hides images in HDR images. The LDR secret image is hidden in the mantissa of the HDR cover image of the same size through a hidden network, and recovered through an extraction network in the receiver. Experimental results show that the proposed algorithm has advantages in security, robustness and capacity compared with other “hiding image in image” algorithms.

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References

  1. Fridrich, J.: Steganography in Digital Media: Principles, Algorithms, and Applications. Cambridge University Press (2009)

  2. JSteg [CP]. http://zooid.org/paul/crypto/jsteg/2009-8-5

  3. Filler, T., Judas, J., Fridrich, J.: Minimizing embedding impact in steganography using trellis coded quantization. Proc. SPIE, Media Forens. Secur. II, 0501–0514 (2010)

    Google Scholar 

  4. Pevny, T, Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: International Workshop on Information Hiding. Springer, Berlin, Heidelberg, pp. 161–177 (2010)

  5. Duan, X., Li, B., Xie, Z., Yue, D., Ma, Y.: High-capacity information hiding based on residual network. Tech. Rev. IETE 38(1), 172–183 (2021). (Web)

    Article  Google Scholar 

  6. Yang, J.H., Ruan, D.Y., Huang, J.W., Kang, X.G., Shi, Y.Q.: An embedding cost learning framework using GAN. IEEE Trans. Inf. Forens. Secur. 15, 839–851 (2020). https://doi.org/10.1109/tifs.2019.2922229

    Article  Google Scholar 

  7. Zhang, R., Dong, S.Q., Liu, J.Y.: Invisible steganography via generative adversarial networks. Multimedia Tools Appl. 78(7), 8559–8575 (2019). https://doi.org/10.1007/s11042-018-6951-z

    Article  Google Scholar 

  8. Zhou, Z.L., Cao, Y., Wang, M.M., Fan, E.M., Wu, Q.M.J.: Faster-RCNN based robust coverless information hiding system in cloud environment. IEEE Access 7, 179891–179897 (2019). https://doi.org/10.1109/access.2019.2955990

    Article  Google Scholar 

  9. Liu, L., Meng, L., Peng, Y., Wang, X.: A Data Hiding Scheme Based on U-Net and Wavelet Transform. Knowl. Based Syst. 223, 107022 (2021). (Web)

    Article  Google Scholar 

  10. Baluja, S.: Hiding images in plain sight: deep steganography. Adv. Neural. Inf. Process. Syst. 30, 2069–2079 (2017)

    Google Scholar 

  11. Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: International Workshop on Information Forensics and Security, pp. 234–239 (2012)

  12. V. Holub, J. Fridrich. Digital image steganography using universal distortion. Proceedings of ACM Information Hiding and Multimedia Security Workshop, 2013, 59–68.

  13. Li, B., Wang, M., Huang, J., et al.: A new cost functions for spatial image steganography. In: Proceeding of 2014 IEEE International Conference on Image Processing, pp. 4206–4210 (2014)

  14. Westfeld, A.: F5—A steganographic algorithm high capacity despite better steganalysis. Lect. Notes Comput. Sci. 21(37), 289–302 (2001)

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Wu, X., Yang, C.N.: Partial reversible AMBTC-based secret image sharing with steganography. Digital Signal Process. 93, 22–33 (2019). https://doi.org/10.1016/j.dsp.2019.06.016

    Article  Google Scholar 

  17. Subhedar, M.S.: Cover selection technique for secure transform domain image steganography. Iran J. Comput. Sci. 4(4), 241–252 (2021). https://link.springer.com/article/10.1007/s42044-020-00077-9

    Article  Google Scholar 

  18. Volkhonskiy, D., Nazarov, I., Borisenko, B., et al.: Steganographic generative adversarial networks. Inf. Hiding Multimedia Secur. 11433, 201–208 (2017)

    Google Scholar 

  19. Shi, H., Dong, J., Wang, W., et al.: SSGAN: Secure Steganography Based on Generative Adversarial Networks. Springer, Cham (2017)

    Google Scholar 

  20. Tang, W., Tan, S., Li, B., et al.: Automatic steganographic distortion learning using a generative adversarial network. IEEE Signal Process. Lett. 66(99), 1 (2017)

    Google Scholar 

  21. Hu, D., Wang, L., Jiang, W., et al.: A novel image steganography method via deep convolutional generative adversarial networks. IEEE Access 6, 38303–38314 (2018)

    Article  Google Scholar 

  22. Baldi, P., Guyon, G., Dror, V., et al.: Autoencoders, unsupervised learning and deep architectures. Workshop Confer. Proc. 27, 37–50 (2012)

    Google Scholar 

  23. Baluja, S.: Hiding images in plain sight: deep steganography. Proc. Adv. Neural Inf. Process. Syst. (NIPS) 30, 2069–2079 (2017)

    Google Scholar 

  24. Rehman, A.U., Rahim, R., Nadeem, S., et al.: End-to-end trained CNN encoder-decoder networks for image steganography (2018)

  25. Zhang, R., Dong, S., Liu, J.: Invisible steganography via generative adversarial networks. Multimedia Tools Appl. (2019)

  26. Duan, X., Jia, K., Li, B., et al.: Reversible image steganography scheme based on a U-net structure. IEEE Access 7, 9317–9323 (2019)

    Google Scholar 

  27. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation.” In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham (2015)

  28. Cheng, Y.M., Wang, C.M.: A Novel Approach to Steganography in High- Dynamic-Range Images. IEEE Multimedia 16(3), 70–80 (2009). (Web)

    Article  Google Scholar 

  29. Yu, C.M., Wu, K.C., Wang, C.M.: A distortion-free data hiding scheme for high dynamic range images. Multimedia Tools Appl. 32(5), 225–236 (2011)

    Google Scholar 

  30. Chang, C.C., Nguyen, T.S., Lin, C.C.: Distortion-free data embedding scheme for high dynamic range images. J. Electron. Sci. Technol. 01, 22–28 (2013)

    Google Scholar 

  31. Lin, Y., Wang, C., Chen, W., et al.: A novel data hiding algorithm for high dynamic range images. IEEE Trans. Multimedia 19(1), 196–211 (2017)

    Article  Google Scholar 

  32. He, X., Zhang, W., Zhang, H., Ma, L., Li, Y.: Reversible data hiding for high dynamic range images using edge information. Multimedia Tools Appl. 78(20), 29137–29160 (2018). (Web)

    Article  Google Scholar 

  33. Gao, X., Pan, Z., Gao, E., Fan, G.: Reversible data hiding for high dynamic range images using two-dimensional prediction-error histogram of the second time prediction. Signal Process. 173, 107579 (2020). (Web)

    Article  Google Scholar 

  34. Bai, Y., Jiang, G., Zhu, Z., Xu, H., Song, Y.:Reversible data hiding scheme for high dynamic range images based on multiple prediction error expansion. Signal Process. Image Commun. 91 (2021). Web.

  35. Ke, Y., Liu, J., Zhang, M.Q., Su, T.T., Yang, X.Y.: Steganography Security: Principle and Practice. IEEE Access 6, 73009–73022 (2018). (Web)

    Article  Google Scholar 

  36. Technical Introduction to OpenEXR. https://openexr.com/en/latest/TechnicalIntroduction.html

  37. Xiao, X., Shen, L., Luo, Z., et al.: Weighted Res-UNet for high-quality retina vessel segmentation. In: 9th International Conference on Information Technology in Medicine and Education (ITME), Hang zhou, China, pp. 327–331 (2018)

  38. Goljan, M., Fridrich, J., Cogranne, R.: Rich model for Steganalysis of color images. In: IEEE International Workshop on Information Forensics and Security, Atlanta, GA, USA, pp. 185–190 (2014). https://doi.org/10.1109/WIFS.2014.7084325

  39. Liu. Y., Lai, W., Chen, Y., et al.: Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline. Proceedings of the IEEE Computer Society Conference on CVPR, pp. 1648-1657 (2020). https://github.com/alex04072000/SingleHDR

  40. https://hdrihaven.com/hdris

  41. https://hdri-skies.com

  42. http://scarlet.stanford.edu/brian/hdr/hdr.html

  43. Narwaria, M., et al.: HDR-VDP-2.2: a calibrated method for objective quality prediction of high-dynamic range and standard images. J. Electron. Imaging 24(1), 010501 (2015)

    Article  Google Scholar 

  44. Chen, F., Xing, Q., Liu, F.: Technology of Hiding and Protecting the Secret Image Based on Two-Channel Deep Hiding Network. IEEE Access 8, 21966–21979 (2020). (Web)

    Article  Google Scholar 

  45. Van, T.P., Dinh, T.H., Thanh, T.M.:Simultaneous convolutional neural network for highly efficient image steganography. In: 2019 19th International Symposium on Communications and Information Technologies (ISCIT), pp. 410–15 (2019). Web

  46. Lin, Y.T., Wang, C.M., Chen, W.S., Lin, F.P., Lin, W.: A Novel Data Hiding Algorithm for High Dynamic Range Images. IEEE Trans. Multimedia 19(1), 196–211 (2017). (Web)

    Article  Google Scholar 

  47. Li, M.T., Huang, N.C., Wang, C.M.: A data hiding scheme for high dynamic range images. Int. J. Innov. Comput. Inf. Control 7(5), 2021–2035 (2011)

    Google Scholar 

  48. Liu, L., Meng, L., Peng, Y., et al.: A data hiding scheme based on U-Net and wavelet transform. Knowl.-Based Syst. 223, 107022 (2021)

    Article  Google Scholar 

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  1. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Yongqing Huo, Yan Qiao & Yaohui Liu

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  1. Yongqing Huo
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  2. Yan Qiao
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Contributions

Yongqing Huo gave guidance to the idea and experiments and is one of the main manuscript writers. Yan Qiao designed and conducted most of the experiments. YaoHui Liu helped to complete the experiments and revise some of the manuscript.

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Huo, Y., Qiao, Y. & Liu, Y. A deep learning-based steganography method for high dynamic range images. Vis Comput 40, 7887–7903 (2024). https://doi.org/10.1007/s00371-023-03214-0

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