End-to-end Trained CNN Encode-Decoder Networks for Image Steganography

2017/11/20 by Atique ur Rehman, Rehman, Atique ur, Rafia Rahim +5 · 1 citation
Computer Science · #Advanced Steganography and Watermarking Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Multimedia (cs.MM)

paper · pdf · doi:10.48550/arxiv.1711.07201

openalex publication_date 2017/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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