Image Steganography Detection Based on Multilayer Perceptual Convolution and Channel Weighting
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摘要: 针对目前图像隐写检测模型中线性卷积层对高阶特征表达能力有限，以及各通道特征图没有区分的问题，该文构建了一个基于多层感知卷积和通道加权的卷积神经网络(CNN)隐写检测模型。该模型使用多层感知卷积(Mlpconv)代替传统的线性卷积，增强隐写检测模型对高阶特征的表达能力；同时引入通道加权模块，实现根据全局信息对每个卷积通道赋予不同的权重，增强有用特征并抑制无用特征，增强模型提取检测特征的质量。实验结果表明，该检测模型针对不同典型隐写算法及不同嵌入率，相比Xu-Net, Yedroudj-Net, Zhang-Net均有更高的检测准确率，与最优的Zhu-Net相比，准确率提高1.95%～6.15%。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%.
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表 1 Yedroudj-Net[12]修改预处理层前后准确率(%)
检测模型 WOW S-UNIWARD 0.2 bpp 0.4 bpp 0.2 bpp 0.4 bpp Yedroudj-Net[12] 72.20 85.90 63.30 77.20 改进预处理层的Yedroudj-Net 77.21 87.54 68.81 81.91 
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表 3 通道加权前后模型的检测准确率(%)
本文模型 WOW S-UNIWARD 0.2bpp 0.4bpp 0.2bpp 0.4bpp 通道加权前 80.72 89.59 76.94 87.66 通道加权后 81.25 90.15 77.65 88.10
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