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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)

Abstract

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.

Keywords

Steganography CNN Encoder-decoder Deep neural networks 

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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

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