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

Feng Liu, Xuan Zhou, Xuehu Yan, Yuliang Lu and Shudong Wang
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Feng Liu: College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
Xuan Zhou: College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
Xuehu Yan: College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
Yuliang Lu: College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
Shudong Wang: College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China

Mathematics, 2021, vol. 9, issue 2, 1-13

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.

Keywords: steganalysis; convolutional neural network; diverse filter module; squeeze-and-excitation module (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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