Deep Neural Networks based Invisible Steganography for Audio-into-Image Algorithm

TL;DR

This paper introduces a deep learning-based method for hiding audio within images, outperforming traditional techniques in preserving data integrity and increasing hidden audio length.

Contribution

It presents a novel joint neural network architecture for audio-in-image steganography, a less explored area compared to image-in-image methods.

Findings

Effective hiding of audio in images with high data integrity

Significant increase in maximum hidden audio length

Outperforms traditional steganography approaches

Abstract

In the last few years, steganography has attracted increasing attention from a large number of researchers since its applications are expanding further than just the field of information security. The most traditional method is based on digital signal processing, such as least significant bit encoding. Recently, there have been some new approaches employing deep learning to address the problem of steganography. However, most of the existing approaches are designed for image-in-image steganography. In this paper, the use of deep learning techniques to hide secret audio into the digital images is proposed. We employ a joint deep neural network architecture consisting of two sub-models: the first network hides the secret audio into an image, and the second one is responsible for decoding the image to obtain the original audio. Extensive experiments are conducted with a set of 24K images and the VIVOS Corpus audio dataset. Through experimental results, it can be seen that our method is more effective than traditional approaches. The integrity of both image and audio is well preserved, while the maximum length of the hidden audio is significantly improved.