End-to-End Trained CNN Encoder-Decoder Networks for Image Steganography

  • Atique ur RehmanEmail author
  • Rafia RahimEmail author
  • Shahroz NadeemEmail author
  • Sibt ul HussainEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11132)


All the existing image steganography methods use manually crafted features to hide binary payloads into cover images. This leads to small payload capacity and image distortion. Here we propose a convolutional neural network based encoder-decoder architecture for embedding of images as payload. To this end, we make following three major contributions: (i) we propose a deep learning based generic encoder-decoder architecture for image steganography; (ii) we introduce a new loss function that ensures joint end-to-end training of encoder-decoder networks; (iii) we perform extensive empirical evaluation of proposed architecture on a range of challenging publicly available datasets (MNIST, CIFAR10, PASCAL-VOC12, ImageNet, LFW) and report state-of-the-art payload capacity at high PSNR and SSIM values.


Steganography CNN Encoder-decoder Deep neural networks 


  1. 1.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.-F.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)Google Scholar
  2. 2.
    Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)CrossRefGoogle Scholar
  3. 3.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS, vol. 9, pp. 249–256 (2010)Google Scholar
  4. 4.
    Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: 2012 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 234–239. IEEE (2012)Google Scholar
  5. 5.
    Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: Workshop on faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition (2008)Google Scholar
  6. 6.
    Hussain, M., Hussain, M.: A survey of image steganography techniques (2013)Google Scholar
  7. 7.
    Islam, S., Modi, M.R., Gupta, P.: Edge-based image steganography. EURASIP J. Inf. Secur. 2014(1), 8 (2014)CrossRefGoogle Scholar
  8. 8.
    Krizhevsky, A., Nair, V., Hinton, G.: The CIFAR-10 dataset (2014)Google Scholar
  9. 9.
    LeCun, Y., Cortes, C., Burges, C.J.: The MNIST database of handwritten digits (1998)Google Scholar
  10. 10.
    Subhedar, M.S., Mankar, V.H.: Current status and key issues in image steganography: a survey. Comput. Sci. Rev. 13, 95–113 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Reveal (Recognition, Vision and Learning) LabNational University of Computer and Emerging Sciences (NUCES-FAST)IslamabadPakistan

Personalised recommendations