摘要
可见水印是一种常用的数字图像版权保护手段。分析可见水印去除结果可以验证图像上水印的有效性,并为水印设计者提供设计或添加水印的参考和启发。目前,大多数的水印去除方法都是基于自然图像的研究,而文档图像在生活中也被广泛使用,但由于缺乏公开的文档图像去水印数据集,相关文档图像的水印去除研究较少。为了探究水印去除方法在文档图像上的水印去除效果,构建了一个文档图像水印去除数据集(SDIWRD)。在对文档图像可见水印去除的研究中发现,使用已有的水印去除方法得到的水印去除结果中容易留下水印主体伪影或者轮廓伪影。为了解决这个问题,提出了一种基于全局与局部特征的二阶段文档图像可见水印去除模型(RWRNet),该模型采用由粗到细的二阶段的半实例归一化编解码器架构。在粗略阶段,使用全局与局部特征提取模块增强对全局空间特征的捕捉能力,同时保留对局部细节信息的提取能力,从而帮助进行水印去除;在细化阶段,细化网络共享粗略阶段权重,并构建循环特征融合模块来充分挖掘粗略阶段编解码器的重要特征,为细化阶段提供丰富的上下文信息,帮助进行细致的水印去除。此外,还结合了结构相似性损失来帮助获取更好的视觉质量。所提方法在SDIWRD数据集上进行了实验,实验结果显示PSNR达到了41.21 dB,SSIM达到了99.07%,RMSE降低至3.64,优于现有水印去除方法。另外也在公开的CLWD彩色水印去除数据集进行了实验,实验结果显示PNSR达到了39.31dB,SSIM达到98.81%,RMSE降低至3.50,也优于现有水印去除方法。实验结果证明了所提方法具有良好的泛化性和去水印的能力,能有效减轻水印伪影。最后还提出了一些防止水印去除的建议,在相关网站1)可公开访问所提出的方法和数据集。
Visible watermark is a common digital image copyright protection measure.Analysis of the removal results of watermarks can verify the effectiveness of the watermarks on images and provide reference and inspiration for watermark designers to design or add them.Currently,most watermark removal methods are based on research on natural images,while document images are also widely used in daily life.However,due to the lack of publicly available datasets for removing watermarks from document images,research on removing watermarks from such images is relatively limited.To explore the effectiveness of watermark removal methods on document images,a dataset for removing watermarks from single document images,the single document image watermark removal dataset(SDIWRD),is constructed.In the research on the removal of watermarks in document images,it is found that the removal results of existing watermark removal methods often leave watermark artifacts,such as main body artifacts or outline artifacts.To address this problem,a two-stage watermark removal model based on global and local features is proposed,which uses a two-stage half-instance normalized encoder-decoder architecture from coarse to fine.In the coarse stage,a global and local feature extraction module is designed to enhance the capture of global spatial features while preserving the extraction of local detail information,thus helping with watermark removal.In the fine stage,the fine network shares the weights of the coarse stage and constructs a recurrent feature fusion module to fully explore the important features of the coarse stage encoder and provide rich context information for the fine stage,helping with detailed watermark removal.In addition,a structure similarity loss is used to improve the visual quality of the removed watermark.The proposed method is tested on the SDIWRD dataset,and the results show that the peak signal-to-noise ratio(PSNR)is 41.21 dB,the structural similarity(SSIM)is 99.07%,and the root mean square error(RMSE)is 3.64,which are better than existing methods.In addition,the proposed method is also tested on the publicly available CLWD color watermark removal dataset,and the results showethat the PSNR is 39.31 dB,the SSIM is 98.81%,and the RMSE is 3.50,which are also better than existing watermark removal methods.These experimental results demonstrate that the proposed method has good generalization and can effectively alleviate the problem of watermark artifacts.Finally,some suggestions for preventing watermark removal are also proposed.The proposed method and dataset can be publicly accessed at the corresponding website.
作者
赵江锋
和红杰
陈帆
杨树斌
ZHAO Jiangfeng;HE Hongjie;CHEN Fan;YANG Shubin(School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China;School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China)
出处
《计算机科学》
CSCD
北大核心
2024年第2期172-181,共10页
Computer Science
基金
国家自然科学基金(61872303,U1936113)。
关键词
可见水印
水印去除
全局与局部特征提取
循环特征融合
文档图像
Visible watermark
Watermark removal
Global and local feature extraction
Recurrent feature fusion
Document image
