摘要
图像隐私保护主要应用于云计算领域,而针对图像或视频的识别任务一般需要其视觉可见,因而往往忽略了隐私保护问题。为了解决这类问题,受到基于压缩感知(compressed sensing,CS)的稀疏表示分类识别算法对于遮挡或污染图像具有较强鲁棒性的启发,提出了一种单层CS采样的扩展模型,使得经过多层CS采样编码后的图像虽然质量退化、内容逐渐变得不可辨别,但依然能够用于图像识别,达到视觉隐私保护的目的。为了能够对多层CS采样编码图像进行图像内容隐私保护度的有效评价,基于人类视觉系统(human visual system,HVS),利用多层CS图像对比度和图像视觉结构退化的特点,通过度量图像对比度和提取图像局部二进制模式(local binary pattern, LBP)特征,提出了面向多层CS图像的内容隐私保护度评价模型(MCS-CPPE)。通过在构造的三大数据集上进行与人眼视觉相关性的实验,验证了所提出的模型有较好的预测性能和效果。
At present, image privacy-preserving is mainly applied to the field of cloud computing, and since the recognition tasks for images or videos generally need to be visually visible, the privacy-preserving problem is often ignored. To solve this kind of problem, inspired by sparse representation for classification based on compressed sensing(CS) which is robust at the images with occlusions and disguises, an extended model of single-layer CS sampling is proposed to ensure the image is degraded and the content is gradually indiscernible after multilayer CS sampling and encoding, which can still be used for image recognition and achieve the purpose of privacy-preserving. To be able to effectively evaluate image content privacy-preserving for multilayer CS sampling and coding, a content privacy-preserving evaluation(MCS-CPPE) model for multilayer CS images based on human visual system(HVS) is proposed, which by measuring image contrast and extracting local binary pattern(LBP) feature because of degradation of contrast and image visual structure. Experiments with human visual correlation on the three constructed databases show that the proposed model has a better prediction performance and effect.
作者
汤正
刘佶鑫
孙宁
韩光
李晓飞
TANG Zheng;LIU Jixin;SUN Ning;HAN Guang;LI Xiaofei(Engineering Research Center of Wideband Wireless Communication Technology,Ministry of Education,Nanjing University of Posts and Telecommunications,Nanjing 210003,Jiangsu,China;College of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,Jiangsu,China)
出处
《陕西师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2020年第2期58-68,共11页
Journal of Shaanxi Normal University：Natural Science Edition
基金
江苏省自然科学基金(BK20180088)
中国博士后科学基金(2019M651916)
江苏省研究生科研与实践创新计划(KYCX18_0919)。
关键词
多层压缩感知
图像内容隐私保护度
对比度
LBP特征
人类视觉系统
multilayer compressed sensing
image content privacy-preserving evaluation
contrast
LBP feature
human visual system
