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Regular Paper

Universal Image Steganalysis Based on Convolutional Neural Network with Global Covariance Pooling

Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou 510006, China
School of Cyberspace Security, Dongguan University of Technology, Dongguan 523808, China
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Abstract

Recently, steganalytic methods based on deep learning have achieved much better performance than traditional methods based on handcrafted features. However, most existing methods based on deep learning are specially designed for one image domain (i.e., spatial or JPEG), and they often take long time to train. To make a balance between the detection performance and the training time, in this paper, we propose an effective and relatively fast steganalytic network called US-CovNet (Universal Steganalytic Covariance Network) for both the spatial and JPEG domains. To this end, we carefully design several important components of US-CovNet that will significantly affect the detection performance, including the high-pass filter set, the shortcut connection and the pooling layer. Extensive experimental results show that compared with the current best steganalytic networks (i.e., SRNet and J-YeNet), US-CovNet can achieve the state-of-the-art results for detecting spatial steganography and have competitive performance for detecting JPEG steganography. For example, the detection accuracy of US-CovNet is at least 0.56% higher than that of SRNet in the spatial domain. In the JPEG domain, US-CovNet performs slightly worse than J-YeNet in some cases with the degradation less than 0.78%. However, the training time of US-CovNet is significantly reduced, which is less than 1/4 and 1/2 of SRNet and J-YeNet respectively.

Keywords

convolutional neural networksteganographysteganalysisglobal covariance pooling

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References

[1]

Fridrich J, Kodovsky J. Rich models for steganalysis of digital images. IEEE Transactions on Information Forensics and Security, 2012, 7(3): 868-882. DOI: 10.1109/TIFS.2012.2190402.
Crossref Google Scholar
[2]

Kodovsky J, Fridrich J, Holub V. Ensemble classifiers for steganalysis of digital media. IEEE Transactions on Information Forensics and Security, 2012, 7(2): 432-444. DOI: 10.1109/TIFS.2011.2175919.
Crossref Google Scholar
[3]

Holub V, Fridrich J. Random projections of residuals for digital image steganalysis. IEEE Transactions on Information Forensics and Security, 2013, 8(12): 1996-2006. DOI: 10.1109/TIFS.2013.2286682.
Crossref Google Scholar
[4]
Denemark T, Sedighi V, Holub V, Cogranne R, Fridrich J. Selection-channel-aware rich model for steganalysis of digital images. In Proc. the IEEE International Workshop on Information Forensics and Security, December 2014, pp. 48-53. DOI: 10.1109/WIFS.2014.7084302.
Crossref
[5]
Song X, Liu F, Yang C, Luo X, Zhang Y. Steganalysis of adaptive JPEG steganography using 2D Gabor filters. In Proc. the 3rd ACM Workshop on Information Hiding and Multimedia Security, June 2015, pp. 15-23. DOI: 10.1145/2756601.2756608.
Crossref
[6]

Denemark T, Boroumand M, Fridrich J. Steganalysis features for content-adaptive JPEG steganography. IEEE Transactions on Information Forensics and Security, 2016, 11(8): 1736-1746. DOI: 10.1109/TIFS.2016.2555281.
Crossref Google Scholar
[7]
Tan S, Li B. Stacked convolutional auto-encoders for steganalysis of digital images. In Proc. the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, December 2014. DOI: 10.1109/APSIPA.2014.7041565.
Crossref
[8]
Qian Y, Dong J, Wang W, Tan T. Deep learning for steganalysis via convolutional neural networks. In Proc. the 2015 SPIE/IS & T Electronic Imaging Symposium, February 2015, Article No. 94090J. DOI: 10.1117/12.2083479.
Crossref
[9]

Xu G, Wu H Z, Shi Y Q. Structural design of convolutional neural networks for steganalysis. IEEE Signal Processing Letters, 2016, 23(5): 708-712. DOI: 10.1109/LSP.2016.2548421.
Crossref Google Scholar
[10]

Ye J, Ni J, Yi Y. Deep learning hierarchical representations for image steganalysis. IEEE Transactions on Information Forensics and Security, 2017, 12(11): 2545-2557. DOI: 10.1109/TIFS.2017.2710946.
Crossref Google Scholar
[11]
Huang J, Ni J, Wan L, Yan J. A customized convolutional neural network with low model complexity for JPEG steganalysis. In Proc. the ACM Workshop on Information Hiding and Multimedia Security, July 2019, pp. 198-203. DOI: 10.1145/3335203.3335734.
Crossref
[12]

Boroumand M, Chen M, Fridrich J. Deep residual network for steganalysis of digital images. IEEE Transactions on Information Forensics and Security, 2019, 14(5): 1181-1193. DOI: 10.1109/TIFS.2018.2871749.
Crossref Google Scholar
[13]
Yedroudj M, Comby F, Chaumont M. Yedroudj-Net: An efficient CNN for spatial steganalysis. In Proc. the IEEE International Conference on Acoustics, Speech and Signal Processing, April 2018, pp. 2092-2096. DOI: 10.1109/ICASSP.2018.8461438.
Crossref
[14]

Ye D, Jiang S, Li S, Liu C. Faster and transferable deep learning steganalysis on GPU. Journal of Real-Time Image Processing, 2019, 16(3): 623-633. DOI: 10.1007/s11554-019-00870-1.
Crossref Google Scholar
[15]

Li S, Ye D, Jiang S, Liu C, Niu X, Luo X. Anti-steganalysis for image on convolutional neural networks. Multimedia Tools and Applications, 2020, 79(7): 4315-4331. DOI: 10.1007/s11042-018-7046-6.
Crossref Google Scholar
[16]

Jiang S, Ye D, Huang J, Shang Y, Zheng Z. Smart-Steganogaphy: Light-weight generative audio steganography model for smart embedding application. Journal of Networking and Computer Applications, 2020, 165: Article No. 102689. DOI: 10.1016/j.jnca.2020.102689.
Crossref Google Scholar
[17]
Kodovský J, Fridrich J, Holub V. On dangers of overtraining steganography to incomplete cover model. In Proc. the 13th ACM Multimedia Workshop on Multimedia and Security, September 2011, pp. 69-76. DOI: 10.1145/2037252.2037266.
Crossref
[18]

Zhang R, Zhu F, Liu J, Liu G. Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis. IEEE Transactions on Information Forensics and Security, 2020, 15: 1138-1150. DOI: 10.1109/TIFS.2019.2936913.
Crossref Google Scholar
[19]
Li Q, Feng G, Wu H, Zhang X. Ensemble steganalysis based on deep residual network. In Proc. the 18th International Workshop on Digital-Forensics and Watermarking, November 2019, pp. 84-95. DOI: 10.1007/978-3-030-43575-2_7.
Crossref
[20]

Su A, Zhao X. Boosting image steganalysis under universal deep learning architecture incorporating ensemble classification strategy. IEEE Signal Processing Letters, 2019, 26(12): 1852-1856. DOI: 10.1109/LSP.2019.2950081.
Crossref Google Scholar
[21]

Kim J, Park H, Park J. CNN-based image steganalysis using additional data embedding. Multimedia Tools and Applications, 2020, 79(1/2): 1355-1372. DOI: 10.1007/s11042-019-08251-3.
Crossref Google Scholar
[22]
Li Q, Shao Z, Tan S, Zeng J, Li B. Non-structured pruning for deep-learning based steganalytic frameworks. In Proc. the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, November 2019, pp. 1735-1739. DOI: 10.1109/APSIPAASC47483.2019.9023018.
Crossref
[23]
Deng X, Chen B, Luo W, Luo D. Fast and effective global covariance pooling network for image steganalysis. In Proc. the ACM Workshop on Information Hiding and Multimedia Security, July 2019, pp. 230-234. DOI: 10.1145/3335203.3335739.
Crossref
[24]
Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proc. the 32nd International Conference on Machine Learning, July 2015, pp. 448-456.
[25]
Li P, Xie J, Wang Q, Gao Z. Towards faster training of global covariance pooling networks by iterative matrix square root normalization. In Proc. the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 2018, pp. 947-955. DOI: 10.1109/CVPR.2018.00105.
Crossref
[26]
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In Proc. the IEEE Conference on Computer Vision and Pattern Recognition, June 2016, pp. 770-778. DOI: 10.1109/CVPR.2016.90.
Crossref
[27]
Wu S, Zhong S, Liu Y. Steganalysis via deep residual network. In Proc. the 22nd IEEE International Conference on Parallel and Distributed Systems, December 2016, pp. 1233-1236. DOI: 10.1109/ICPADS.2016.0167.
Crossref
[28]
Li P, Xie J, Wang Q, Zuo W. Is second-order information helpful for large-scale visual recognition? In Proc. the IEEE International Conference on Computer Vision, October 2017, pp. 2089-2097. DOI: 10.1109/ICCV.2017.228.
Crossref
[29]
Bas P, Filler T, Pevný T. "Break our steganographic system": The ins and outs of organizing BOSS. In Proc. the 13th International Workshop on Information Hiding, May 2011, pp. 59-70. DOI: 10.1007/978-3-642-24178-9_5.
Crossref
[30]
He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In Proc. the IEEE International Conference on Computer Vision, December 2015, pp. 1026-1034. DOI: 10.1109/ICCV.2015.123.
Crossref
[31]
Bengio Y, Louradour J, Collobert R, Weston J. Curriculum learning. In Proc. the 26th International Conference on Machine Learning, June 2009, pp. 41-48. DOI: 10.1145/1553374.1553380.
Crossref
[32]

Holub V, Fridrich J, Denemark T. Universal distortion function for steganography in an arbitrary domain. EURASIP Journal on Information Security, 2014, 2014: Article No. 1. DOI: 10.1186/1687-417X-2014-1.
Crossref Google Scholar
[33]
Li B, Wang M, Huang J, Li X. A new cost function for spatial image steganography. In Proc. the IEEE International Conference on Image Processing, October 2014, pp. 4206-4210. DOI: 10.1109/ICIP.2014.7025854.
Crossref
[34]
Holub V, Fridrich J. Designing steganographic distortion using directional filters. In Proc. the IEEE International Workshop on Information Forensics and Security, December 2012, pp. 234-239. DOI: 10.1109/WIFS.2012.6412655.
Crossref
[35]

Guo L, Ni J, Shi Y Q. Uniform embedding for efficient JPEG steganography. IEEE Transactions on Information Forensics and Security, 2014, 9(5): 814-825. DOI: 10.1109/TIFS.2014.2312817.
Crossref Google Scholar
[36]

Denemark T, Fridrich J, Comesaña-Alfaro P. Improving selection-channel-aware steganalysis features. Electronic Imaging, 2016, 28: Article No. art00016. DOI: 10.2352/ISSN.2470-1173.2016.8.MWSF-080.
Crossref Google Scholar
[37]

Elsken T, Metzen J H, Hutter F. Neural architecture search: A survey. Journal of Machine Learning Research, 2019, 20: Article No. 55.
Crossref Google Scholar
Journal of Computer Science and Technology
Volume 37 Issue 5,
September 2022
Pages 1134-1145
DOI: 10.1007/s11390-021-0572-0
Cite this article:
Deng X-Q, Chen B-L, Luo W-Q, et al. Universal Image Steganalysis Based on Convolutional Neural Network with Global Covariance Pooling. Journal of Computer Science and Technology, 2022, 37(5): 1134-1145. https://doi.org/10.1007/s11390-021-0572-0

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Received: 21 April 2020
Accepted: 29 June 2021
Published: 30 September 2022
©Institute of Computing Technology, Chinese Academy of Sciences 2022
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