Hiding Information in Big Data based on Deep Learning

TL;DR

This paper introduces a deep learning-based method for hiding and extracting secret messages within big data, leveraging existing data as carriers and ensuring security through the complexity of deep black box models.

Contribution

It presents a novel approach that uses existing big data as carriers for secret messages, overcoming limitations of previous methods and enhancing security and capacity.

Findings

Hides secret messages safely and securely.

Supports unlimited data and message capacity.

Operates quickly and conveniently without data limitations.

Abstract

The current approach of information hiding based on deep learning model can not directly use the original data as carriers, which means the approach can not make use of the existing data in big data to hiding information. We proposed a novel method of information hiding in big data based on deep learning. Our method uses the existing data in big data as carriers and uses deep learning models to hide and extract secret messages in big data. The data amount of big data is unlimited and thus the data amount of secret messages hided in big data can also be unlimited. Before opponents want to extract secret messages from carriers, they need to find the carriers, however finding out the carriers from big data is just like finding out a box from the sea. Deep learning models are well known as deep black boxes in which the process from the input to the output is very complex, and thus the deep learning model for information hiding is almost impossible for opponents to reconstruct. The results also show that our method can hide secret messages safely, conveniently, quickly and with no limitation on the data amount.