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

Atique ur Rehman, Rafia Rahim, Muhammad Shahroz Nadeem, Sibt ul Hussain

ECCVW 2018 pp. 723-729

doi:10.1007/978-3-030-11018-5_64 /eccvw/2018/rehman2018eccvw-endtoend/

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.

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Cite

Text

Rehman et al. "End-to-End Trained CNN Encoder-Decoder Networks for Image Steganography." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11018-5_64

Markdown

[Rehman et al. "End-to-End Trained CNN Encoder-Decoder Networks for Image Steganography." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/rehman2018eccvw-endtoend/) doi:10.1007/978-3-030-11018-5_64

BibTeX

@inproceedings{rehman2018eccvw-endtoend,
  title     = {{End-to-End Trained CNN Encoder-Decoder Networks for Image Steganography}},
  author    = {Rehman, Atique ur and Rahim, Rafia and Nadeem, Muhammad Shahroz and Hussain, Sibt ul},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2018},
  pages     = {723-729},
  doi       = {10.1007/978-3-030-11018-5_64},
  url       = {https://mlanthology.org/eccvw/2018/rehman2018eccvw-endtoend/}
}