Image Steganalysis via Diverse Filters and Squeeze-and-Excitation Convolutional Neural Network

Feng Liu,Xuan Zhou,Xuehu Yan,Shudong Wang,Yuliang Lu

doi:10.3390/math9020189

Feng Liu, Xuan Zhou + Show 3 more

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https://doi.org/10.3390/math9020189

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Journal: Mathematics	Publication Date: Jan 19, 2021
Citations: 10	License type: CC BY 4.0

Affiliation: National University of Defense Technology

Abstract

Steganalysis is a method to detect whether the objects contain secret messages. With the popularity of deep learning, using convolutional neural networks (CNNs), steganalytic schemes have become the chief method of combating steganography in recent years. However, the diversity of filters has not been fully utilized in the current research. This paper constructs a new effective network with diverse filter modules (DFMs) and squeeze-and-excitation modules (SEMs), which can better capture the embedding artifacts. As the essential parts, combining three different scale convolution filters, DFMs can process information diversely, and the SEMs can enhance the effective channels out from DFMs. The experiments presented that our CNN is effective against content-adaptive steganographic schemes with different payloads, such as S-UNIWARD and WOW algorithms. Moreover, some state-of-the-art methods are compared with our approach to demonstrate the outstanding performance.

Highlights

With the rapid development of information technology, covert communication methods using steganography have attracted increasing attention in recent years
With the improvement of steganography, it is more difficult to find out the embedding traces in objects, as the secret information is hidden in the texture area of the image with contentadaptive steganographic algorithms, such as HUGO [1], S-UNIWARD [2], WOW [3], HILL [4], MiPoD [5], JUNIWARD [6], UERD [7], ASO [8] and so on [9,10,11]
We have proved the validity of DFSE-Net on the data sets generated in Section 4.1, and the experimental results are shown in Figure 7 and Table 5

Summary

Introduction

With the rapid development of information technology, covert communication methods using steganography have attracted increasing attention in recent years. The core idea of these adaptive image steganography algorithms is to design the embedded distortion cost function, so as to separately measure the impact of each pixel modification in an image for the steganography security. It means that the security issues of steganography have been transformed into the issues of optimizing the distortion cost, which can guide the embedding operation of steganography by calculating the minimized embedding distortion to maximize the security of the steganography. The aim of steganography is to hide secret information in objects to covert communication. Steganalysis is a relatively challenging task as the changes of cover objects are almost impossible to be recognized by human eyes

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Results

Conclusion