Color image steganalysis based on embedding change probabilities in differential channels

Xiangyang Luo,Xiaofeng Song,Fenlin Liu,Yuhan Kang,Chunfang Yang,Jie Wang

doi:10.1177/1550147720917826

Xiangyang Luo, Xiaofeng Song + Show 4 more

Open Access

https://doi.org/10.1177/1550147720917826

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Abstract

It is a potential threat to persons and companies to reveal private or company-sensitive data through the Internet of Things by the color image steganography. The existing rich model features for color image steganalysis fail to utilize the fact that the content-adaptive steganography changes the pixels in complex textured regions with higher possibility. Therefore, this article proposes a variant of spatial rich model feature based on the embedding change probabilities in differential channels. The proposed feature is extracted from the residuals in the differential channels to reduce the image content information and enhance the stego signals significantly. Then, the embedding change probability of each element in the differential channels is added to the corresponding co-occurrence matrix bin to emphasize the interference of the residuals in textured regions to the improved co-occurrence matrix feature. The experimental results show that the proposed feature can significantly improve the detection performances for the WOW and S-UNIWARD steganography, especially when the payload size is small. For example, when the payload size is 0.05 bpp, the detection errors can be reduced respectively by 5.20% and 4.90% for WOW and S-UNIWARD by concatenating the proposed feature to the color rich model feature CRMQ1.

Highlights

With the development of 5G, more and more images will be transmitted in the Internet of Things
We can apply the steganalysis algorithms proposed for gray images to the detection of each color channel, combine the detection results of multiple color channels to determine if the color image contains secret
The existing color image steganalysis algorithms can be roughly divided into the following five categories according to the extracted steganalysis feature: 1. Because color image steganography will affect the statistical characteristics of multiple channels, some color image steganalysis algorithms combine the features extracted from different channels to improve the detection accuracy

Summary

Introduction

With the development of 5G, more and more images will be transmitted in the Internet of Things. In 2014, Denemark et al.[28] incorporated the embedding change probabilities when calculating the residual co-occurrence matrices of gray image, proposed the selection-channel-aware rich model feature—maxSRM—which significantly improves the steganalysis performance for content-adaptive gray image steganography. Activated by this idea, this article proposed a variant of spatial rich model feature based on embedding change probabilities in differential channels. The experimental results show that the proposed feature significantly reduces the detection errors for the WOW and S-UNIWARD steganography, especially when the payload size is small

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Experimental results and analysis

Conclusion