基于尺寸变换的图像级特征增强隐写分析方法  

Scaling-based image-level feature enhancement for steganalysis

作　　者：刘绪龙 李伟祥 林凯清 李斌[1,2,3] LIU Xulong;LI Weixiang;LIN Kaiqing;LI Bin(College of Electronic and Information Engineering,Shenzhen University,Shenzhen 518060,China;Guangdong Key Laboratory of Intelligent Information Processing,Shenzhen 518060,China;Shenzhen Key Laboratory of Media Security,Shenzhen 518060,China)

机构地区：[1]深圳大学电子与信息工程学院,深圳518060 [2]广东省智能信息处理重点实验室,深圳518060 [3]深圳市媒体信息内容安全重点实验室,深圳518060

出　　处：《网络空间安全科学学报》2024年第1期101-112,共12页Journal of Cybersecurity

基　　金：国家自然科学基金(U22B2047,62202310,U23B2022);中国博士后科学基金(2022M722192)。

摘　　要：随着深度学习的快速发展,基于深度学习的图像隐写分析技术研究取得了显著进展。然而,在残差特征提取及增强方面,传统图像预处理增强技术往往导致隐写信号的减弱,使得简单的图像预处理方法难以适配于隐写分析。对此,现有的深度学习隐写分析研究倾向于在不损害图像原有信息的基础上,设计固定的滤波核或对残差卷积层优化学习,缺乏对图像层面的隐写特征增强策略的可行性探讨。针对这一现象,提出了一种新颖高效的图像级特征增强隐写分析方法,通过最近邻插值算法扩大图像尺寸,在保留原始隐写信号的基础上进一步拓展分布相同的嵌入信号,从而增强模型对隐写残差特征的提取能力,无须对现有隐写分析流程做出显著改动即可有效提高隐写痕迹的可检测性。实验结果显示,所提方法能够显著提升模型在多种隐写算法下的检测准确率,尤其对于低嵌入率,其准确率最高可提升2.81%。该方法证实了图像层面预处理在隐写残差特征增强上的有效性,为深度学习隐写分析的图像残差特征提取提供了新的研究视角。With the rapid development of deep learning,the research of image steganalysis techniques based on deep learning have made significant progress.However,in terms of residual feature extraction and enhancement,traditional image preprocessing enhance-ment techniques often inevitably weaken the steganographic signals,making it difficult to adapt simple image preprocessing methods to steganalysis.Therefore,existing deep learning steganalysis research tends to design fixed filter kernels or optimize the learning of the residual convolutional layers,resulting in a relative lack of exploration of steganographic feature enhancement at the input image level.In this regard,a novel and efficient method for image-level feature enhancement in steganalysis is proposed.By employing the nearest neighbor interpolation algorithm to expand the size of image,the image’s steganographic signals is amplified while maintaining their original distribution.This further enhances the model’s capability of steganographic residual feature extraction,and effectively improves the detectability of steganographic traces without making significant changes to the existing steganalysis process.The experimental re-sults show that the proposed method can significantly improve the model’s detection accuracy under various steganography algorithms,especially for low embedding rate environment where the accuracy can be improved by 2.81%.It confirms the effectiveness of image-level preprocessing on steganographic residual feature enhancement,and provides a new research perspective on image residual feature extraction for deep learning steganalysis models.

关 键 词：隐写分析 深度学习 残差特征增强 图像缩放 最近邻插值 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]