Hiding Data Hiding

TL;DR

This paper introduces a novel approach to data hiding by disguising data embedding and extraction tools as deep neural networks performing style transfer, enhancing covert communication by hiding the very tools used for data hiding.

Contribution

It presents a new method to conceal data hiding tools within neural networks that perform style transfer, making the tools themselves undetectable and more secure.

Findings

The method successfully hides data hiding tools within style transfer neural networks.

Experimental results demonstrate the feasibility and effectiveness of the approach.

The approach outperforms traditional data hiding methods in concealment and robustness.

Abstract

Data hiding is the art of hiding secret data into a cover object such as digital image for covert communication. In this paper, we make the first step towards hiding ``data hiding'', which is totally different from many conventional works that directly embed secret data in a given cover object. In detail, we propose a novel method to disguise data hiding tools, including a data embedding tool and a data extraction tool, as a deep neural network (DNN) with an ordinary task (i.e., style transfer). After training the DNN for both style transfer and data hiding, while the DNN can transfer the style of an image to the target one, it can also hide secret data into a cover image or extract secret data from a stego image. In other words, the tools of data hiding are hidden to avoid arousing suspicion. Experimental results and analysis have shown the feasibility, applicability and superiority of the proposed method.