Perceptual video hashing robust against geometric distortions  

作　　者：XIANG ShiJun YANG JianQuan HUANG JiWu 

机构地区：[1]School of Information Science and Technology,Jinan University,Guangzhou 510632,China [2]Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China [3]School of Information Science and Technology,Sun Yat-sen University,Guangzhou 510275,China [4]State Key Laboratory of Information Security,Institute of Software,Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Science China(Information Sciences)》2012年第7期1520-1527,共8页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 60903177, 61003297);National Basic Research Program of China (Grant No. 2011CB302204);Fundamental Research Funds for the Central Universities (Grant No. 21611408)

摘　　要：In this paper, we propose a robust perceptual hashing algorithm by using video luminance histogram in shape. The underlying robustness principles are based on three main aspects： 1） Since the histogram is independent of position of a pixel, the algorithm is resistant to geometric deformations; 2） the hash is extracted from the spatial Gaussian-filtering low-frequency component for those common video processing operations such as noise corruption, low-pass filtering, lossy compression, etc.; 3） a temporal Gaussian-filtering operation is designed so that the hash is resistant to temporal desynchronization operations, such as frame rate change and dropping. As a result, the hash function is robust to common geometric distortions and video processing operations. Experimental results show that the proposed hashing strategy can provide satisfactory robustness and uniqueness.In this paper, we propose a robust perceptual hashing algorithm by using video luminance histogram in shape. The underlying robustness principles are based on three main aspects： 1） Since the histogram is independent of position of a pixel, the algorithm is resistant to geometric deformations; 2） the hash is extracted from the spatial Gaussian-filtering low-frequency component for those common video processing operations such as noise corruption, low-pass filtering, lossy compression, etc.; 3） a temporal Gaussian-filtering operation is designed so that the hash is resistant to temporal desynchronization operations, such as frame rate change and dropping. As a result, the hash function is robust to common geometric distortions and video processing operations. Experimental results show that the proposed hashing strategy can provide satisfactory robustness and uniqueness.

关 键 词：video hashing geometric distortion HISTOGRAM Gaussian filtering 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP301.6[自动化与计算机技术—计算机科学与技术]