基于中心差分卷积和注意力的空域彩色图像隐写分析  

Spatial Color Image Steganalysis Based on Central Difference Convolution and Attention

作　　者：魏康康 骆伟祺[1,2] 刘明林 WEI Kang-Kang;LUO Wei-Qi;LIU Ming-Lin(School of Computer Science and Engineering,Sun Yat-sen University,Guangzhou 510006,China;Guangdong Key Laboratory of Information Security Technology,Guangzhou 510006,China;School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450002,China)

机构地区：[1]中山大学计算机学院,广东广州510006 [2]广东省信息安全技术重点实验室,广东广州510006 [3]郑州大学网络空间安全学院,河南郑州450002

出　　处：《软件学报》2024年第12期5671-5686,共16页Journal of Software

基　　金：国家自然科学基金(61972430)。

摘　　要：目前,大多数已发表的图像隐写分析方法都是针对灰度图像设计的,因此这些方法无法有效检测广泛应用于社交媒体的彩色图像.为解决这一问题,提出一种基于中心差分卷积和注意力增强的彩色图像隐写分析方法.首先设计一个包含预处理,特征提取和特征分类这3个阶段的主干流.在预处理阶段,对输入的彩色图像进行颜色通道分离,并串联各通道经过SRM滤波后的残差图.在特征提取阶段,构建3个基于中心差分卷积的卷积块来提取更深层的隐写分析特征图.在分类阶段,使用全局协方差池化和带有丢弃操作的两个全连接层来对载体和载密图像进行分类.此外,为了进一步增强主干流在不同时期的特征表达能力,在主干流的前期和后期分别引入一个残差空间注意力增强模块和一个通道注意力增强模块.其中,残差空间注意力增强模块首先使用Gabor滤波核对输入图像进行通道分离卷积再串联相应的残差,然后通过空间注意力机制获取残差特征图的有效信息.而通道注意力增强模块则通过获取通道间的依赖关系来增强模型最后的特征分类能力.进行大量的对比实验,结果表明所提出方法可以显著提高对彩色图像隐写的检测性能,并取得当前最好的结果.此外,还进行相应的消融实验来验证所提出的网络架构的合理性.Currently,most of the published image steganalysis methods are designed for grayscale images,which cannot effectively detect color images widely used in social media.To solve this problem,this study proposes a color image steganalysis method based on central difference convolution and attention enhancement.The proposed method first designs a backbone flow consisting of three stages:preprocessing,feature extraction,and feature classification.In the preprocessing stage,the input color image is color channel-separated,and the residual images after SRM filtering are concatenated through each channel.In the feature extraction stage,the study constructs three convolutional blocks based on central difference convolution to extract deeper steganalysis feature maps.In the classification stage,the study uses global covariance pooling and two fully connected layers with dropout operation to classify the cover and stego images.Additionally,to further enhance the feature expression ability of the backbone flow at different stages,it introduces a residual spatial attention enhancement module and a channel attention enhancement module at the early and late stages of the backbone flow,respectively.Specifically,the residual spatial attention enhancement module first uses Gabor filter kernels to perform channel-separated convolution on the input image and then obtains the effective information of the residual feature map through the spatial attention mechanism.The channel attention enhancement module enhances the final feature classification ability of the model by obtaining the dependence relationship between channels.A large number of comparative experiments have been conducted,and the results show that the proposed method can significantly improve the detection performance of color image steganography and achieve the best results currently.In addition,the study also conducts corresponding ablation experiments to verify the rationality of the proposed network architecture.

关 键 词：隐写分析 隐写 彩色图像 卷积神经网络 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]