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.
@article{arxiv.1711.07201,
title = {End-to-end Trained CNN Encode-Decoder Networks for Image Steganography},
author = {Atique ur Rehman and Rafia Rahim and M Shahroz Nadeem and Sibt ul Hussain},
journal= {arXiv preprint arXiv:1711.07201},
year = {2017}
}