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

Atique ur Rehman, Rafia Rahim, Shahroz Nadeem, Sibt ul Hussain; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

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.