Abstract
Steganographer detection aims to find guilty user who spread images containing secret information among many innocent users. Feature extraction is an important step in steganographer detection. The challenge is that the existing steganalytic feature extraction of steganographer detection is mostly based on manually designed features. A steganographer detection algorithm based on multi-scale convolutional networks (MSCNN_SD) is proposed to automatically extract steganalytic features in the paper. MSCNN_SD introduces the residual maps and quantization truncation idea of classical SRM into deep convolution network. MSCNN_SD uses a set of filters to extract rich residual information and uses a number of quantized truncation combinations are introduced to discretize the residual maps. Finally, two parallel deep learning subnets are used to learn the features of different scale residuals. The simulation results illustrate that the proposed MSCNN_SD method is superior to the state-of-the-art method. MSCNN_SD uses a well-trained model of one steganography payload, which can detect different steganography and payloads used by steganographer. At the same time, MSCNN_SD has a good detection effect when the steganographer uses the hybrid steganography strategy .
This research was supported by CCF-Tencent Open Fund WeBank Special Funding, the National Key R&D Program of China (No. 2017YFB0802803).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ge, C., Susilo, W., Wang, J., et al.: A key-policy attribute-based proxy re-encryption without random oracles. Comput. J. 59(7), 970–982 (2016)
Wang, G, Liu, H, Wang, C, et al.: Revocable attribute based encryption in cloud storage. IEEE Trans. Dependable Secure Comput. (2021)
Fang, L., Susilo, W., Ge, C., et al.: Public key encryption with keyword search secure against keyword guessing attacks without random oracle. Inf. Sci. 238, 221–241 (2013)
Li, F., Wen, M., Lei, J., Ren, Y.: Efficient steganographer detection over social networks with sampling reconstruction. Peer-to-Peer Netw. Appl. 11(5), 924–939 (2017). https://doi.org/10.1007/s12083-017-0603-3
Ker, A.D., Pevny, T., et al.: A new paradigm for steganalysis via clustering. In: Proceedings of the Conference on Media Watermarking, Secruity, and Forensics III, pp. 78800U-1–78800U-13 (2011)
Ker, A.D, Pevny, T., et al.: The steganographer is the outlier: realistic large-scale steganalysis. IEEE Trans. Inf. Forensics Secur. 9, 424–1435 (2014)
Zheng, M, Zhang, S.H., Wu, S., et al.: Steganographer detection via deep residual network. In: Proceedings of the IEEE International Conference on Multimedia & Expo, pp. 235–240. IEEE Computer Society, ICME, Buet (2017)
Qian, Y., Dong, J., Wang, W., Tan, T.: Feature learning for steganalysis using convolutional neural networks. Multimedia Tools Appl. 77(15), 19633–19657 (2017). https://doi.org/10.1007/s11042-017-5326-1
Zhong, S.H., Wang, Y.T., Ren, T.W., et al.: Steganographer detection via multi-scale embedding probability estimation. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) (2019)
Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)
Ye, J., Ni, J., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inf. Forensics Secur. 12(11), 2545–2557 (2017)
Tang, W.X., Tan, S.Q., Li, B., et al.: Automatic steganographic distortion learning using a generative adversarial network. IEEE Signal Process. Lett. 24(99), 1547–1551 (2017)
Zheng, M.J., Zhong, S.H., Wu, S.T., et al.: Steganographer detection based on multiclass dilated residual networks. In: Proceedings of the ACM International Conference on Multimedia Retrieval, pp. 300–308. ACM Multimedia, Yokohama (2018)
Zeng, J.S., Tan, S.Q., Li, B., et al.: Pre-training via fitting deep neural network to rich-model features extraction procedure and its effect on deep learning for steganalysis. Electron. Imaging 2017(7), 44–49 (2017)
Pevný, T., Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: Böhme, R., Fong, P.W.L., Safavi-Naini, R. (eds.) IH 2010. LNCS, vol. 6387, pp. 161–177. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16435-4_13
Holub, V., Fridrich, J., et al.: Designing steganographic distortion using directional filters. In: IEEE Workshop on Information Forensic and Security. pp. 234–239. IEEE, Tenerife (2012)
Holub, V., Fridrich, J., et al.: Digital image steganography using universal distortion. In: Proceedings of the First ACM Workshop on Information Hiding and Multimedia Security, pp.59–68. IH&MMSEC, Montpellier (2013)
Xu, G., Wu, H.Z., Shi, Y.Q., et al.: Structural design of convolutional neural networks for steganalysis. IEEE Signal Process. Lett. 23(5), 708–712 (2016)
Zheng, M.J., Zhong, S.H., Wu, S.T., et al.: Steganographer detection based on multiclass dilated residual networks. In: Proceedings of the ACM International Conference on Multimedia Retrieval, pp. 300–308. .ICMR, Yokohama (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, J., Dong, C., Zhang, F., Lei, M., Bai, X. (2021). MSCNN: Steganographer Detection Based on Multi-Scale Convolutional Neural Networks. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12937. Springer, Cham. https://doi.org/10.1007/978-3-030-85928-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-85928-2_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85927-5
Online ISBN: 978-3-030-85928-2
eBook Packages: Computer ScienceComputer Science (R0)