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基于多层感知卷积和通道加权的图像隐写检测

叶学义 郭文风 曾懋胜 张珂绅 赵知劲

叶学义, 郭文风, 曾懋胜, 张珂绅, 赵知劲. 基于多层感知卷积和通道加权的图像隐写检测[J]. 电子与信息学报, 2022, 44(8): 2949-2956. doi: 10.11999/JEIT210537
引用本文: 叶学义, 郭文风, 曾懋胜, 张珂绅, 赵知劲. 基于多层感知卷积和通道加权的图像隐写检测[J]. 电子与信息学报, 2022, 44(8): 2949-2956. doi: 10.11999/JEIT210537
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

基于多层感知卷积和通道加权的图像隐写检测

doi: 10.11999/JEIT210537
基金项目: 国家自然科学基金(U19B2016, 60802047)
详细信息
    作者简介:

    叶学义:男,1973年生,副教授,研究方向为图像处理、模式识别、信息隐藏

    郭文风:女,1997年生,硕士生,研究方向为图像隐写检测

    曾懋胜:男,1998年生,硕士生,研究方向为计算机视觉

    张珂绅:男,1996年生,硕士生,研究方向为毫米波检测

    赵知劲:女,1959年生,教授,研究方向为信号处理、软件无线电

    通讯作者:

    郭文风 gwf@hdu.edu.cn

  • 中图分类号: TN911.73; TP309.2

Image Steganography Detection Based on Multilayer Perceptual Convolution and Channel Weighting

Funds: The National Natural Science Foundation of China (U19B2016, 60802047)
  • 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%。
  • 图  1  Yedroudj-Net[12]CNN架构

    图  2  本文检测模型

    图  3  线性卷积层

    图  4  Mlpconv层

    图  5  SE模块结构图

    图  6  模型所提取部分特征图

    图  7  不同Mlpconv层数实验结果图

    表  1  Yedroudj-Net[12]修改预处理层前后准确率(%)

    检测模型WOWS-UNIWARD
    0.2 bpp0.4 bpp0.2 bpp0.4 bpp
    Yedroudj-Net[12]72.2085.9063.3077.20
    改进预处理层的Yedroudj-Net77.2187.5468.8181.91
    下载: 导出CSV

    表  2  本文模型与其他模型的对比结果(%)

    检测模型WOWS-UNIWARD
    0.2 bpp0.4 bpp0.2 bpp0.4 bpp
    Xu-Net[10]67.6079.3060.8072.80
    Yedroudj-Net[12]72.2085.9063.3077.20
    Zhang-Net[13]76.7088.2071.5084.70
    本文模型80.7289.5976.9487.66
    下载: 导出CSV

    表  3  通道加权前后模型的检测准确率(%)

    本文模型WOWS-UNIWARD
    0.2bpp0.4bpp0.2bpp0.4bpp
    通道加权前80.7289.5976.9487.66
    通道加权后81.2590.1577.6588.10
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-08
  • 修回日期:  2021-12-21
  • 录用日期:  2021-12-27
  • 网络出版日期:  2022-01-13
  • 刊出日期:  2022-08-17

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