JPEG Steganography with Embedding Cost Learning and Side-Information Estimation

TL;DR

This paper introduces a novel JPEG steganography framework using GANs that adaptively learns embedding costs and leverages estimated side-information to enhance security against CNN-based steganalysis.

Contribution

It proposes the JS-GAN framework that automatically learns content-adaptive embedding costs and incorporates estimated side-information to improve JPEG steganography security.

Findings

JS-GAN increases detection error by 2.58% over J-UNIWARD.

Estimated side-information further improves security by 11.25%.

The method effectively learns content-adaptive embedding costs.

Abstract

A great challenge to steganography has arisen with the wide application of steganalysis methods based on convolutional neural networks (CNNs). To this end, embedding cost learning frameworks based on generative adversarial networks (GANs) have been proposed and achieved success for spatial steganography. However, the application of GAN to JPEG steganography is still in the prototype stage; its anti-detectability and training efficiency should be improved. In conventional steganography, research has shown that the side-information calculated from the precover can be used to enhance security. However, it is hard to calculate the side-information without the spatial domain image. In this work, an embedding cost learning framework for JPEG Steganography via a Generative Adversarial Network (JS-GAN) has been proposed, the learned embedding cost can be further adjusted asymmetrically according to the estimated side-information. Experimental results have demonstrated that the proposed method can automatically learn a content-adaptive embedding cost function, and use the estimated side-information properly can effectively improve the security performance. For example, under the attack of a classic steganalyzer GFR with quality factor 75 and 0.4 bpnzAC, the proposed JS-GAN can increase the detection error 2.58% over J-UNIWARD, and the estimated side-information aided version JS-GAN(ESI) can further increase the security performance by 11.25% over JS-GAN.