Abstract
Nowadays, convolutional neural network (CNN) based steganalysis methods achieved great performance. While those methods are also facing security problems. In this paper, we proposed an attack scheme aiming at CNN based steganalyzer including two different attack methods 1) the LSB-Jstego Gradient Based Attack; 2) LSB-Jstego Evolutionary Algorithms Based Attack. The experiment results show that the attack strategies could achieve 96.02% and 90.25% success ratio separately on the target CNN. The proposed attack scheme is an effective way to fool the CNN based steganalyzer and in addition demonstrates the vulnerability of the neural networks in steganalysis.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bas P, Filler T, Pevnỳ T (2011) Break our steganographic system: the ins and outs of organizing boss. In: International workshop on information hiding, Springer, pp 59–70
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31
Floreano D, Mattiussi C (2008) Bio-inspired artificial intelligence: theories, methods, and technologies. MIT Press, Cambridge
Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7(3):868–882
Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv:14126572
Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. Tech. rep., Citeseer
Kurak C, McHugh J (1992) A cautionary note on image downgrading. In: Proceedings 8th annual computer security applications conference, 1992. IEEE, pp 153–159
Kurakin A, Goodfellow I, Bengio S (2016) Adversarial examples in the physical world. arXiv:160702533
LeCun Y (1998) The mnist database of handwritten digits. http://yannlecuncom/exdb/mnist/
Luo X, Song X, Li X, Zhang W, Lu J, Yang C, Liu F (2016) Steganalysis of hugo steganography based on parameter recognition of syndrome-trellis-codes. Multimed Tools Appl 75(21):13557–13583
Ma Y, Luo X, Li X, Bao Z, Zhang Y (2018) Selection of rich model steganalysis features based on decision rough set α-positive region reduction. IEEE Trans Circuits Syst Video Technol https://doi.org/10.1109/TCSVT.2018.2799243
Moosavi-Dezfooli SM, Fawzi A, Frossard P (2016) Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2574–2582
Moosavi-Dezfooli SM, Fawzi A, Fawzi O, Frossard P (2017) Universal adversarial perturbations. arXiv preprint
Nguyen A, Yosinski J, Clune J (2015) Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 427–436
Pevny T (2011) Detecting messages of unknown length. In: Media watermarking, security, and forensics III, international society for optics and photonics, vol 7880, p 78800T
Pibre L, Pasquet J, Ienco D, Chaumont M (2016) Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover sourcemismatch. Electron Imaging 2016(8):1–11
Qian Y, Dong J, Wang W, Tan T (2015) Deep learning for steganalysis via convolutional neural networks. In: Media watermarking, security, and forensics 2015, international society for optics and photonics, vol 9409, p 94090J
Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Su J, Vargas DV, Kouichi S (2017) One pixel attack for fooling deep neural networks. arXiv:171008864
Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2013) Intriguing properties of neural networks. arXiv:13126199
Upham D (1997) Jsteg. Software available at ftp funet fi
Wang J, Li T, Shi YQ, Lian S, Ye J (2017) Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimed Tools Appl 76(22):23721–23737
Winkler S, Mohandas P (2008) The evolution of video quality measurement: from psnr to hybrid metrics. IEEE Trans Broadcast 54(3):660–668
Wu S, Zhong SH, Liu Y (2017) A novel convolutional neural network for image steganalysis with shared normalization. arXiv:171107306
Xu G, Wu HZ, Shi YQ (2016) Structural design of convolutional neural networks for steganalysis. IEEE Signal Process Lett 23(5):708–712
Ye J, Ni J, Yi Y (2017) Deep learning hierarchical representations for image steganalysis. IEEE Trans Inf Forensics Secur 12(11):2545–2557
Zhang Y, Qin C, Zhang W, Liu f, Luo X (2018) On the fault-tolerant performance for a class of robust image steganography. Signal Process 146:99–111
Acknowledgements
An earlier version of this paper was presented at the 4th International Conference on Cloud Computing and Security, 8-10, June 2018, Haikou, China. This work was partially supported by the National Key Research Development Program of China (2016QY01W0200), the National Natural Science Foundation of China NSFC (U1636101,U1636219, U1736211).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, S., Ye, D., Jiang, S. et al. Anti-steganalysis for image on convolutional neural networks. Multimed Tools Appl 79, 4315–4331 (2020). https://doi.org/10.1007/s11042-018-7046-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-018-7046-6