Image Steganography Detection Based on Multilayer Perceptual Convolution and Channel Weighting

YE Xueyi; GUO Wenfeng; ZENG Maosheng; ZHANG Keshen; ZHAO Zhijin

doi:10.11999/JEIT210537

Volume 44 Issue 8

Aug. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(8): 2949-2956

YE Xueyi, GUO Wenfeng, ZENG Maosheng, ZHANG Keshen, ZHAO Zhijin. Image Steganography Detection Based on Multilayer Perceptual Convolution and Channel Weighting[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2949-2956. doi: 10.11999/JEIT210537

Citation:

YE Xueyi, GUO Wenfeng, ZENG Maosheng, ZHANG Keshen, ZHAO Zhijin. Image Steganography Detection Based on Multilayer Perceptual Convolution and Channel Weighting[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2949-2956. doi: 10.11999/JEIT210537

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Image Steganography Detection Based on Multilayer Perceptual Convolution and Channel Weighting

doi: 10.11999/JEIT210537 cstr: 32379.14.JEIT210537

School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China

Funds: The National Natural Science Foundation of China (U19B2016, 60802047)

Received Date: 2021-06-08
Accepted Date: 2021-12-27
Rev Recd Date: 2021-12-21

Available Online: 2022-01-13

Publish Date: 2022-08-17

Abstract

Abstract

For steganalysis, many studies have shown that convolutional neural networks have better performance than traditional artificially designed features. However, the ability of linear convolution layer to express higher-order features is limited and the feature map of each channel is not distinguished in the existing detection model which based on Convolutional Neural Networks (CNN). To solve these problems, an image steganography detection model based on Multi-layer perceptual convolution and channel weighting is constructed in this paper. The Multi-layer perceptual convolution layer (Mlpconv)is used to replace the traditional linear convolution layer to enhance the expressiveness ability of high-order features of the detection model. The channel weighting module is added to the model, which assigns different weights to each convolution channel based on global information, so that the useful features can be enhanced and the useless features can be suppressed, and the detection features extracted from the quality model can be improved. The experimental results show that the detection accuracy of proposed detection model is higher than that of Xu-Net, Yedroudj-Net, and Zhang-Net for different typical steganography algorithms and different embedding rates. And compared with the optimal Zhu-Net, the accuracy rate is increased by 1.95～6.15%.
- Steganography detection,
- Convolutional Neural Networks (CNN),
- Multi-layer perceptual convolution (Mlpconv),
- Channel weighted

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References(20)

References

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