DDAC：面向卷积神经网络图像隐写分析模型的特征提取方法

王晓丹; 李京泰; 宋亚飞

doi:10.11959/j.issn.1000-436x.2022089

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DDAC：面向卷积神经网络图像隐写分析模型的特征提取方法

学术论文 | 更新时间：2024-06-05

- DDAC：面向卷积神经网络图像隐写分析模型的特征提取方法
- DDAC: a feature extraction method for model of image steganalysis based on convolutional neural network
- 通信学报 2022年43卷第5期页码：68-81
- 作者机构：
  
  空军工程大学防空反导学院，陕西西安 710051
- 作者简介：
  
  [ "王晓丹（1966- ），女，陕西汉中人，博士，空军工程大学教授，主要研究方向为智能信息处理、机器学习" ]
  [ "李京泰（1998- ），男，重庆人，空军工程大学硕士生，主要研究方向为图像隐写分析" ]
  [ "宋亚飞（1988- ），男，河南汝州人，博士，空军工程大学副教授，主要研究方向为机器学习及其在目标识别、入侵检测等领域中的应用" ]
- 基金信息：
  
  国家自然科学基金资助项目(61876189)
- DOI：10.11959/j.issn.1000-436x.2022089
  中图分类号： TP309
- 网络出版日期：2022-05，
  
  纸质出版日期：2022-05-25
- 稿件说明：
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王晓丹, 李京泰, 宋亚飞. DDAC：面向卷积神经网络图像隐写分析模型的特征提取方法[J]. 通信学报, 2022,43(5):68-81.

Xiaodan WANG, Jingtai LI, Yafei SONG. DDAC: a feature extraction method for model of image steganalysis based on convolutional neural network[J]. Journal on communications, 2022, 43(5): 68-81.
王晓丹, 李京泰, 宋亚飞. DDAC：面向卷积神经网络图像隐写分析模型的特征提取方法[J]. 通信学报, 2022,43(5):68-81. DOI： 10.11959/j.issn.1000-436x.2022089.

Xiaodan WANG, Jingtai LI, Yafei SONG. DDAC: a feature extraction method for model of image steganalysis based on convolutional neural network[J]. Journal on communications, 2022, 43(5): 68-81. DOI： 10.11959/j.issn.1000-436x.2022089.

摘要

针对基于卷积神经网络的图像隐写分析方法中使用人工设计的滤波器在特征提取过程中有效性低的问题，提出方向差分自适应组合（DDAC）特征提取方法。在计算中心像素与周围不同方向像素的差分后，使用 1× 1 卷积对方向差分进行线性组合。根据损失对组合参数自适应更新来构建多样化的滤波器，使获取的嵌入信息残差特征更有效。使用截断线性单元提高嵌入信息残差和图像信息残差的比率，加快模型收敛速度并提高残差特征提取能力。实验结果表明，该方法使 Ye-net、Yedroudj-net 模型的准确率在 WOW 和S-UNIWARD数据集中提高1.30%～8.21%。与固定和更新参数SRM滤波器方法相比，测试模型在不同隐写数据集中的准确率提高 0.60%～20.72%，并且训练过程更稳定。对比其他图像隐写分析模型，DDAC-net 具有更高的隐写分析效率。

Abstract

To solve the problem that for image steganalysis based on convolution neural network

manual designed filter kernels were used to extract residual characteristics

but in practice

these kernels filter were not suitable for each steganography algorithm and have worse performance in application

a directional difference adaptive combination (DDAC) method was proposed.Firstly

the difference was calculated between center pixel and each directional pixel around

and 1 × 1 convolution was adopted to achieve linear combinations of directional difference.Since the combination parameters self-adaptively update according to loss function

filter kernels could be more effective in extracting diverse residual characteristics of embedding information.Secondly

truncated linear unit (TLU) was applied to raise the ratio of embedding information residual to image information residual.The model’s coveragence was accelerated and the ability of feature extraction was promoted.Experimental results indicate that substituting the proposed method could improve the accuracy of Ye-net and Yedroudj-net by 1.30%~8.21% in WOW and S-UNIWARD datasets.Compared with fix and adjustable SRM filter kernels methods

the accuracy of test model using DDAC increases 0.60%~20.72% in various datasets

and the training progress was more stable.DDAC-net was proved to be more effective in comparsion with other steganalysis model.

关键词

Keywords

references

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