The development of multimedia and deep learning technology bring new challenges to steganography and steganalysis techniques. Meanwhile, robust steganography, as a class of new techniques aiming to solve the problem of covert communication under lossy channels, has become a new research hotspot in the field of information hiding. To improve the communication reliability and efficiency for current real-time robust steganography methods, a concatenated code, composed of Syndrome–Trellis codes (STC) and cyclic redundancy check (CRC) codes, is proposed in this paper. The enhanced robust adaptive steganography framework proposed is this paper is characterized by a strong error detection capability, high coding efficiency, and low embedding costs. On this basis, three adaptive steganographic methods resisting JPEG compression and detection are proposed. Then, the fault tolerance of the proposed steganography methods is analyzed using the residual model of JPEG compression, thus obtaining the appropriate coding parameters. Experimental results show that the proposed methods have a significantly stronger robustness against compression, and are more difficult to be detected by statistical based steganalytic methods.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Amini, M., Ahmad, M.O., Swamy, M.N.S.: A new locally optimum watermark detection using vector-based hidden markov model in wavelet domain. Signal Process. 137(2017), 213–222 (2017)
Bors, A.G., Pitas, I.: Image watermarking using DCT domain constraints. In: IEEE International Conference on Image Processing. IEEE, pp. 231–234 (1996)
Denemark, T., Fridrich, J.: Steganography with two JPEGs of the same scene. In: ieee international conference on acoustics, speech and signal processing. IEEE, pp. 2117–2121 (2017)
Filler, T., Judas, J., Fridrich, J.: Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans. Inf. Forensics Secur. 6(3), 920–935 (2011)
Guo, L., Ni, J., Shi, Y.Q.: An efficient JPEG steganographic scheme using uniform embedding. In: IEEE international workshop on information forensics and security. IEEE, pp. 169–174 (2012)
Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: IEEE workshop on information forensic and security. IEEE, pp. 234–239 (2012)
Holub, V., Fridrich, J.: Digital steganography using universal distortion function. In: ACM workshop on information hiding and multimedia security. ACM, pp. 59–68 (2013)
Kang, Y.H., Liu, F.L., Yang, C.F., et al.: Color image steganalysis based on residuals of channel differences. Comput. Mater. Continua 59(1), 315–329 (2019)
Koopman, P., Chakravarty, T.: Cyclic redundancy code (CRC) polynomial selection for embedded networks. In: International conference on dependable systems and networks. IEEE, pp. 145–154 (2004)
Parah, S., Sheikh, J., Loan, N., et al.: Robust and blind watermarking technique in DCT domain using inter-block coefficient differencing. Dig. Signal Process. 53, 11–24 (2016)
Pevný, T., Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: ACM international workshop on information hiding. ACM, pp. 161–177 (2010)
Qin, C., He, Z.H., Luo, X.Y., et al.: Reversible data hiding in encrypted image with separable capability and high embedding capacity. Inf. Sci. 465, 285–304 (2018)
Qu, Z.G., Zhu, T.C., Wang, J.W., et al.: A novel quantum stegonagraphy based on brown states. Comput. Mater. Continua 56(1), 47–59 (2018)
Sedighi, V., Cogranne, R., Fridrich, J.: Content-adaptive steganography by minimizing statistical detectability. IEEE Trans. Inf. Forens. Secur. 11(2), 221–234 (2016)
Tsai, J., Huang, W., Kuo, Y.: On the selection of optimal feature region set for robust digital image watermarking. IEEE Trans. Image Process. 20(3), 735–743 (2011)
Tsai, J., Huang, W., Kuo, Y., et al.: Joint robustness and security enhancement for feature-based image watermarking using invariant feature regions. Signal Process. 92(6), 1431–1445 (2012)
Wang, J.W., Ting, L., Luo, X.Y., et al.: Identifying computer generated images based on quaternion central moments in color quaternion wavelet domain. IEEE transactions on circuits and systems for video technology, pp. 1–12 (2018)
Xu, G.S.: Deep convolutional neural network to detect J-UNIWARD. In: ACM workshop on information hiding and multimedia security. ACM, pp. 67–73 (2017)
Ye, J., Ni, J.Q., Yang, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inf. Forens. Secur. 12(11), 2545–2557 (2017)
Zhang, Y., Luo, X.Y., Yang, C.F., et. al.: A JPEG-compression resistant adaptive steganography based on relative relationship between DCT coefficients. In: IEEE international conference on availability, reliability and security. IEEE, pp 461–466 (2015)
Zhang, Y., Luo, X.Y., Yang, C.F., et al.: A framework of adaptive steganography resisting JPEG compression and detection. Secur. Commun. Netw. 9(15), 2957–2971 (2016)
Zhang, Y., Luo, X.Y., Yang, C.F., et al.: Joint JPEG compression and detection resistant performance enhancement for adaptive steganography using feature regions selection. Multimedia Tools Appl. 76(3), 3649–3668 (2017)
Zhang, Y., Qin, C., Zhang, W.M., et al.: On the fault-tolerant performance for a class of robust image steganography. Signal Process. 146, 99–111 (2018)
Zhang, Y., Ye, D.P., Gan, J.J., et al.: An image steganography algorithm based on quantization index modulation resisting scaling attacksand statistical detection. Comput. Mater. Continua 55(1), 59–70 (2018)
Zhang, Y.W., Zhang, W.M., Chen, K.J., et. al.: Adversarial examples against deep neural network based steganalysis. In: ACM workshop on information hiding and multimedia security. ACM, pp. 67–72 (2018)
Zhang, Y., Zhu, X.D., Qin, C., et al.: Dither modulation based adaptive steganography resisting JPEG compression and statistic detection. Multimedia Tools Appl. 77(14), 17913–17935 (2018)
Zhou, Z.L., Yan, M., Wu, Q.M.J.: Coverless image steganography using partial-duplicate image retrieval. Soft Comput. 2, 1–12 (2018)
This work was supported in part by the National Natural Science Foundation of China (NSFC nos. U1804263, U1736214, U1636219, 61872448, 61772549, and 61602508), the National Key R&D Program (nos. 2016YFB0801303, 2016QY01W0105), and the Science and Technology Innovation Talent Project of Henan Province (no. 184200510018).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Zhang, Y., Luo, X., Zhu, X. et al. Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes. J Real-Time Image Proc 17, 115–123 (2020). https://doi.org/10.1007/s11554-019-00905-7
- Robust steganography
- STC–CRC codes
- JPEG compression resistant
- Statistical detection resistant