基于CNN计算局部复杂度的可逆信息隐藏算法  

CNN Based Local Complexity Estimation for Reversible Data Hiding

作　　者：胡润文 项世军[1] 李晓龙 欧博 HU Run-Wen;XIANG Shi-Jun;LI Xiao-Long;OU Bo(College of Information Science and Technology/College of Cyber Security,Jinan University,Guanghou 510632;Insitute of Information Science,Beijing Jiaotong University,Beijing 100044;College of Computer Science and Electronic Enginering,Hunan University,Changsha 410082)

机构地区：[1]暨南大学信息科学技术学院/网络空间安全学院,广州510632 [2]北京交通大学信息科学研究所,北京100044 [3]湖南大学信息科学与工程学院,长沙410082

出　　处：《计算机学报》2024年第4期776-789,共14页Chinese Journal of Computers

基　　金：国家自然科学基金(No.62272197,No.62372037);广东省基础与应用基础研究基金自然科学基金(No.2023A1515011928)资助。

摘　　要：在可逆信息隐藏领域,选择较小的预测误差有助于减少信息嵌入过程引起的失真.现有选择方法主要计算预测误差的局部复杂度,将信息嵌入到局部复杂度小的预测误差中.这些局部复杂度计算方法只使用部分相邻像素计算局部复杂度,且计算方法与预测部分使用的预测器无关,因此其选择性能有待提高.本文提出了一种基于CNN的局部复杂度计算方法LCCN,该方法与预测部分的预测器相关,可以使用更多的相邻像素计算局部复杂度,提高局部复杂度与预测误差的相似性.与现有的局部复杂度计算方法相比,所提LCCN利用了CNN的多感受野特性和深度学习的全局优化能力,能使用更多相邻像素计算局部复杂度,从而选择更多数值较小的预测误差,提高嵌入算法性能.此外,我们还提出了一种适用于LCCN的两阶段RDH方案,该方案可以将LCCN应用于多种嵌入方法.实验结果表明,与现有的几种局部复杂度计算方法相比,本文所提方法在标准测试图像和Kodak数据集上嵌入性能更好,在基于LPVO的嵌入技术中,本文所提LCCN模型嵌入10000bits后图像Lena的PSNR为62.09dB,分别高于LV、FV、EE和LAE算法1.05dB、1.02dB、0.78dB和0.90dB.与现有的先进RDH算法比较,本文所提LCCN模型在不同图像上均取得较好效果.In the reversible data hiding(RDH)community,how to select smaller prediction er-rors to reduce the embedding distortion is a crucial issue.A logical way is to use the neighboring pixels to estimate the local complexity of a target pixel for selection.Previous local complexity calculation methods often use the correlation between neighboring pixels to calculate the local complexity,in which only a small number of neighboring pixels are considered for calculation.In addition,these calculation methods are only related to the embedding methods,which are inde-pendent of the prediction methods.Due to the small calculation range and the separation from the prediction methods,the correlation between prediction errors and local complexities is limited and should be further considered.In this paper,we estimate that the local complexity is the predic-tion of the prediction errors.To better explain the estimation,the classical calculation method named local absolute error(LAE)is adopted,which greatly reveals that the local complexity is highly correlated with the prediction error.Based on the discovery,we propose a new CNN-based method titled LCCN to better calculate the local complexity of pixels.To our knowledge,we are the first to calculate the local complexity by using the deep learning-based method.Compared with previous local complexity calculation methods,the proposed LCCN can use more neighbor-ing pixels as the context by exploiting the multi-receptive fields of CNN and the global optimiza-tion capacities of deep learning.As a bonus,satisfactory performance in selecting those smaller prediction errors for RDH can be achieved,e.g.,the proposed LCCN can achieve lower mean and variance of the sorted prediction errors than the classical local complexity calculation meth-ods,including the local variance(LV),forward variance(FV),error energy(EE),and LAE.In addition,the correlation coefficient between prediction error and local complexity is higher and the value range of the sorted prediction is more concentrated.Furthermor

关 键 词：可逆信息隐藏 局部复杂度 深度学习 卷积神经网络 预测误差 隐私计算 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]