Enhancing Image Steganalysis with Adversarially Generated Examples

  • Kevin Alex ZhangEmail author
  • Kalyan Veeramachaneni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11527)


The goal of image steganalysis is to counter steganography algorithms which attempt to hide a secret message within an image file. We focus specifically on blind image steganalysis in the spatial domain which involves detecting the presence of secret messages in image files without knowing the exact algorithm used to embed them. In this paper, we demonstrate that we can achieve better performance on the blind steganalysis task by training the YeNet architecture with adversarially generated examples provided by SteganoGAN.


Steganalysis Steganography Deep learning 


  1. 1.
    Baluja, S.: Hiding images in plain sight: deep steganography. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30, pp. 2069–2079. Curran Associates, Inc. (2017)Google Scholar
  2. 2.
    Bas, P., Filler, T., Pevný, T.: “Break our steganographic system”: the ins and outs of organizing BOSS. In: Filler, T., Pevný, T., Craver, S., Ker, A. (eds.) IH 2011. LNCS, vol. 6958, pp. 59–70. Springer, Heidelberg (2011). Scholar
  3. 3.
    Boehm, B.: StegExpose - a tool for detecting LSB steganography. CoRR abs/1410.6656 (2014)Google Scholar
  4. 4.
    Dumitrescu, S., Wu, X., Memon, N.: On steganalysis of random LSB embedding in continuous-tone images 3, 641–644 (2002).
  5. 5.
    Dumitrescu, S., Wu, X., Wang, Z.: Detection of LSB steganography via sample pair analysis. In: Petitcolas, F.A.P. (ed.) IH 2002. LNCS, vol. 2578, pp. 355–372. Springer, Heidelberg (2003). Scholar
  6. 6.
    Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012). Scholar
  7. 7.
    Fridrich, J., Goljan, M., Du, R.: Reliable detection of LSB steganography in color and grayscale images. In: Proceedings of the 2001 Workshop on Multimedia and Security: New Challenges, pp. 27–30. ACM (2001).
  8. 8.
    Hayes, J., Danezis, G.: Generating steganographic images via adversarial training. In: NIPS (2017)Google Scholar
  9. 9.
    Lin, T., et al.: Microsoft COCO: common objects in context. CoRR abs/1405.0312 (2014)Google Scholar
  10. 10.
    Wu, P., Yang, Y., Li, X.: StegNet: mega image steganography capacity with deep convolutional network. Future Internet 10, 54 (2018). Scholar
  11. 11.
    Ye, J., Ni, J., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inf. Forensics Secur. 12(11), 2545–2557 (2017). Scholar
  12. 12.
    Zhang, K.A., Cuesta-Infante, A., Xu, L., Veeramachaneni, K.: SteganoGAN: high capacity image steganography with gans. CoRR abs/1901.03892 (2019).
  13. 13.
    Zhu, J., Kaplan, R., Johnson, J., Fei-Fei, L.: HiDDeN: hiding data with deep networks. CoRR abs/1807.09937 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.MITCambridgeUSA

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