Image data hiding with multi-scale autoencoder network

TL;DR

This paper introduces a multi-scale autoencoder network for image data hiding that improves embedding in high-level features, significantly reducing error rates and network complexity compared to previous models.

Contribution

The paper proposes a novel multi-scale autoencoder architecture for image steganography, enhancing feature learning and reducing errors and model size.

Findings

Empirically achieves 0% bit-error rate

Uses fewer network parameters than previous models

Effective in hiding secrets in internet memes

Abstract

mage steganography is the process of hiding information which can be text, image, or video inside a cover image. The advantage of steganography over cryptography is that the intended secret message does not attract attention and is thus more suitable for secret communication in a highly-surveillant environment such as civil disobedience movements. Internet memes in social media and messaging apps have become a popular culture worldwide, so this folk custom is a good application scenario for image steganography. We try to explore and adopt the steganography techniques on the Internet memes in this work. We implement and improve the HiDDeN model by changing the Conv-BN-ReLU blocks convolution layer with a multiscale autoencoder network so that the neural network learns to embed message bits in higher-level feature space. Compared to methods that convolve feature filters on the row-pixel domain, our proposed MS-Hidden network learns to hide secrets in both low-level and high-level image features. As a result, the proposed model significantly reduces the bit-error rate to empirically 0% and the required network parameters are much less than the HiDDeN model.