基于多層感知卷積和通道加權(quán)的圖像隱寫(xiě)檢測(cè)
doi: 10.11999/JEIT210537
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杭州電子科技大學(xué)通信工程學(xué)院 杭州 310018
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(U19B2016, 60802047)
Image Steganography Detection Based on Multilayer Perceptual Convolution and Channel Weighting
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School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Funds: The National Natural Science Foundation of China (U19B2016, 60802047)
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摘要: 針對(duì)目前圖像隱寫(xiě)檢測(cè)模型中線性卷積層對(duì)高階特征表達(dá)能力有限，以及各通道特征圖沒(méi)有區(qū)分的問(wèn)題，該文構(gòu)建了一個(gè)基于多層感知卷積和通道加權(quán)的卷積神經(jīng)網(wǎng)絡(luò)(CNN)隱寫(xiě)檢測(cè)模型。該模型使用多層感知卷積(Mlpconv)代替?zhèn)鹘y(tǒng)的線性卷積，增強(qiáng)隱寫(xiě)檢測(cè)模型對(duì)高階特征的表達(dá)能力；同時(shí)引入通道加權(quán)模塊，實(shí)現(xiàn)根據(jù)全局信息對(duì)每個(gè)卷積通道賦予不同的權(quán)重，增強(qiáng)有用特征并抑制無(wú)用特征，增強(qiáng)模型提取檢測(cè)特征的質(zhì)量。實(shí)驗(yàn)結(jié)果表明，該檢測(cè)模型針對(duì)不同典型隱寫(xiě)算法及不同嵌入率，相比Xu-Net, Yedroudj-Net, Zhang-Net均有更高的檢測(cè)準(zhǔn)確率，與最優(yōu)的Zhu-Net相比，準(zhǔn)確率提高1.95%～6.15%。
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關(guān)鍵詞:
- 隱寫(xiě)檢測(cè) /
- 卷積神經(jīng)網(wǎng)絡(luò) /
- 多層感知卷積 /
- 通道加權(quán)
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%. -
表 1 Yedroudj-Net[12]修改預(yù)處理層前后準(zhǔn)確率(%)
檢測(cè)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 改進(jìn)預(yù)處理層的Yedroudj-Net 77.21 87.54 68.81 81.91 
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表 3 通道加權(quán)前后模型的檢測(cè)準(zhǔn)確率(%)
本文模型 WOW S-UNIWARD 0.2bpp 0.4bpp 0.2bpp 0.4bpp 通道加權(quán)前 80.72 89.59 76.94 87.66 通道加權(quán)后 81.25 90.15 77.65 88.10
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