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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
  • [1] LIU Jia, KE Yan, ZHANG Zhuo, et al. Recent advances of image steganography with generative adversarial networks[J]. IEEE Access, 2020, 8: 60575–60597. doi: 10.1109/ACCESS.2020.2983175
    [2] PEVNY T, BAS P, and FRIDRICH J. Steganalysis by subtractive pixel adjacency matrix[J]. IEEE Transactions on information Forensics and Security, 2010, 5(2): 215–224. doi: 10.1109/TIFS.2010.2045842
    [3] FRIDRICH J and KODOVSKY J. Rich models for steganalysis of digital images[J]. IEEE Transactions on Information Forensics and Security, 2012, 7(3): 868–882. doi: 10.1109/tifs.2012.2190402
    [4] 付章杰, 李恩露, 程旭, 等. 基于深度学习的图像隐写研究进展[J]. 计算机研究与发展, 2021, 58(3): 548–568. doi: 10.7544/issn1000-1239.2021.20200360

    FU Zhangjie, LI Enlu, CHENG Xu, et al. Recent advances in image steganography based on deep learning[J]. Computer Research and Development, 2021, 58(3): 548–568. doi: 10.7544/issn1000-1239.2021.20200360
    [5] SHARIFZADEH M, ALORAINI M, and SCHONFELD D. Adaptive batch size image merging steganography and quantized Gaussian image steganography[J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 867–879. doi: 10.1109/TIFS.2019.2929441
    [6] LAISHRAM D and TUITHUNG T. A novel minimal distortion-based edge adaptive image steganography scheme using local complexity[J]. Multimedia Tools and Applications, 2021, 80(1): 831–854. doi: 10.1007/S11042-020-09519-9
    [7] 陈君夫, 付章杰, 张卫明, 等. 基于深度学习的图像隐写分析综述[J]. 软件学报, 2021, 32(2): 551–578. doi: 10.13328/j.cnki.jos.006135

    CHEN Junfu, FU Zhangjie, ZHANG Weiming, et al. Review of image steganalysis based on deep learning[J]. Journal of Software, 2021, 32(2): 551–578. doi: 10.13328/j.cnki.jos.006135
    [8] TAN Shunquan and LI Bin. Stacked convolutional auto-encoders for steganalysis of digital images[C]. Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, Siem Reap, Cambodia, 2014: 1–4.
    [9] QIAN Yinlong, DONG Jing, WANG Wei, et al. Deep learning for steganalysis via convolutional neural networks[J]. SPIE, 2015, 9409.
    [10] XU Guanshuo, WU Hanzhou, and SHI Yunqing. Structural design of convolutional neural networks for steganalysis[J]. IEEE Signal Processing Letters, 2016, 23(5): 708–712. doi: 10.1109/LSP.2016.2548421
    [11] YE Jian, NI Jiangqun, and YI Yang. Deep learning hierarchical representations for image steganalysis[J]. IEEE Transactions on Information Forensics and Security, 2017, 12(11): 2545–2557. doi: 10.1109/TIFS.2017.2710946
    [12] YEDROUDJ M, COMBY F, and CHAUMONT M. Yedrouj-Net: An efficient CNN for spatial steganalysis[C]. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2018: 2092–2096.
    [13] ZHANG Ru, ZHU Feng, LIU Jianyi, et al. Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis[J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 1138–1150. doi: 10.1109/TIFS.2019.2936913
    [14] HOLUB V and FRIDRICH J. Designing steganographic distortion using directional filters[C]. 2012 IEEE International Workshop on Information Forensics and Security (WIFS), Costa Adeje, Spain, 2012: 234–239.
    [15] HOLUB V, FRIDRICH J, and DENEMARK T. Universal distortion function for steganography in an arbitrary domain[J]. EURASIP Journal on Information Security, 2014, 2014: 1. doi: 10.1186/1687-417X-2014-1
    [16] MEMISEVIC R, ZACH C, HINTON G E, et al. Gated softmax classification[C]. The 23rd International Conference on Neural Information Processing Systems, Red Hook, USA, 2010: 1603–1611.
    [17] LIN Min, CHEN Qiang, and YAN Shuicheng. Network in network[Z]. ArXiv: 1312.4400, 2013.
    [18] HU JIE, SHEN Li, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011–2023. doi: 10.1109/TPAMI.2019.2913372
    [19] BAS P, FILLER T, and TOMÁS PEVNÝ T. “Break Our Steganographic System”: The ins and outs of organizing BOSS[C]. Information Hiding 13th International Conference, Prague, Czech Republic, 2011: 59–70.
    [20] GLOROT X and BENGIO Y. Understanding the difficulty of training deep feedforward neural networks[J]. Journal of Machine Learning Research, 2010, 9: 249–256.
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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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