基于复杂度匹配和注意力机制的图像隐写分析算法
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作者单位:

南京信息工程大学数字取证教育部工程研究中心

基金项目:

国家自然科学基金(U22B2062,62172232, 62202234,62172233);中国博士后科学基金 2023M741778


A image steganalysis algorithm based on complexity matching and attention mechanism
Author:
Affiliation:

Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology

Fund Project:

National Natural Science Foundation of China(U22B2062,62172232, 62202234,62172233);Supported by China Postdoctoral Science Foundation 2023M741778

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    摘要:

    近年来,图像隐写分析算法快速发展,一系列先进的基于深度学习的隐写分析模型不断被提出。然而,现有隐写分析算法无法对图像纹理复杂和纹理平滑区域实现精准权重分配,阻碍了其进一步发展。为了解决隐写分析算法特征提取和分析不稳定的问题,本文提出一种基于复杂度匹配和注意力机制的隐写图像检测算法。首先,利用隐写算法倾向于在纹理复杂区域嵌入的特点,设计复杂度匹配策略,独立提取纹理复杂块和纹理平滑块特征,聚集强隐写信号区域,提高模型对于微弱隐写信号的提取能力;然后,通过卷积注意力分配模块,有效合理分配对于不同图像区域的注意力,提高网络对于重要特征和纹理区域的关注度。实验结果表明,在BOSSBase v1.01和Alaska2两个公开数据集上,相对于现有模型,所提出的算法对多个隐写算法的隐写分析性能均有提升。

    Abstract:

    In recent years, image steganalysis algorithms have developed rapidly, and a series of advanced deep learning-based steganalysis models have been continuously proposed. However, existing steganalysis algorithms are unable to achieve precise weight allocation in areas with complex and smooth image textures, hindering their further development. To address the instability in feature extraction and analysis of steganalysis algorithms, this paper proposes a image steganalysis algorithm based on complexity matching and attention mechanism. First, using the characteristics of steganography algorithm tends to embed in texture complex region, designing complexity matching strategy, independently extracting texture complex block and texture smooth block features, aggregating strong steganographic signal region, and improving the extraction ability of the model for weak steganographic signals. Second, through the design of the improved convolutional attention mechanism, the attention to different image regions is effectively and reasonably allocated to improve the network for the important features and texture regions. The experimental results show that the proposed algorithm improves the steganalysis performance of several steganographic algorithms on two public datasets, BOSSBase v1.01 and Alaska2, compared with the existing models.

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邓宜洋,张翔,卢俊杰,王帆,付章杰.基于复杂度匹配和注意力机制的图像隐写分析算法[J].南京信息工程大学学报,,():

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历史
  • 收稿日期:2024-12-11
  • 最后修改日期:2025-01-14
  • 录用日期:2025-02-13

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