基于几何均值分解和结构相似度的同源视频时间域复制-粘贴篡改快速检测及恢复方法
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摘要
针对现有方法中篡改检测效率不高、定位不精确的问题,提出了一种基于几何均值分解(GMD)和结构相似度(SSIM)的同源视频复制-粘贴快速篡改检测及恢复的方法。首先,将视频转换为灰度图像序列。其次,将几何均值分解作为检测特征,提出了一个基于块的搜索策略来定位复制序列的起始帧。此外,算法首次将结构相似度用于度量视频两帧之间的相似度,并利用结构相似度对搜索策略得到的起始帧进行复检。由于复制视频序列对应两帧之间的相似度高于未篡改序列对应两帧之间的相似度,提出了一个基于结构相似度的从粗到精的方法来定位复制视频序列的末尾帧。最后,对视频进行恢复。与其他几种经典算法进行对比,实验结果表明,所提方法不仅能够检测经过复制-粘贴篡改操作的视频,而且能准确地定位复制-粘贴序列。此外,该方法在检测精度、召回率和检测时间上有较大提升。
Aiming at the problem of low efficiency of tampering detection and accuracy of location, a homologous video copy-move tampering detection and recovering method based on Geometric Mean Decomposition( GMD) and Structural SIMilarity( SSIM) was proposed. Firstly, the videos were translated into grayscale image sequences. Then, the geometric mean decomposition was adopted as a feature and a block-based search strategy was put forward to locate the starting frame of the duplicated sequences. In addition, SSIM was first extended to measure the similarity between two frames of a video. The starting frame of duplicated sequences was rechecked by using the structural similarity. Since the value of similarity between duplicated frames is higher than that between the normal inter-frames, a coarse-to-fine method based on SSIM was put forward to locate the tail frame. Finally, the video was recovered. In comparison with other classical algorithms, the experimental results show that the proposed method can not only achieve detection of copy-move forgery but also accurately detect and localize duplicated clips in different kinds of videos. Besides, the method has a great improvement in terms of precision, recall and computation time.
Aiming at the problem of low efficiency of tampering detection and accuracy of location, a homologous video copy-move tampering detection and recovering method based on Geometric Mean Decomposition( GMD) and Structural SIMilarity( SSIM) was proposed. Firstly, the videos were translated into grayscale image sequences. Then, the geometric mean decomposition was adopted as a feature and a block-based search strategy was put forward to locate the starting frame of the duplicated sequences. In addition, SSIM was first extended to measure the similarity between two frames of a video. The starting frame of duplicated sequences was rechecked by using the structural similarity. Since the value of similarity between duplicated frames is higher than that between the normal inter-frames, a coarse-to-fine method based on SSIM was put forward to locate the tail frame. Finally, the video was recovered. In comparison with other classical algorithms, the experimental results show that the proposed method can not only achieve detection of copy-move forgery but also accurately detect and localize duplicated clips in different kinds of videos. Besides, the method has a great improvement in terms of precision, recall and computation time.
引文
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[13]KOBAYASHI M,OKABE T,SATO Y.Detecting forgery from static-scene video based on inconsistency in noise level functions[J].IEEE Transactions on Information Forensics and Security,2010,5(4):883-892.
[14]STAMM M C,LIN W S,LIU K J R.Temporal forensics and antiforensics for motion compensated video[J].IEEE Transactions on Information Forensics and Security,2012,7(4):1315-1329.
[15]CHEN R,DONG Q,REN H,et al.Video forgery detection based on non-subsampled Contourlet transform and gradient information[J].Information Technology Journal,2012,11(10):1456-1462.
[2]LIN G-S,CHANG J-F.DETECTION of frame duplication forgery in videos based on spatial and temporal analysis[J].International Journal of Pattern Recognition and Artificial Intelligence,2012,26(7):1-18.
[3]QIN Y,SUN G,ZHANG X.Exposing digital forgeries in video via motion vectors[J].Journal of Computer Research and Development,2009,46(z1):227-233.(秦运龙,孙广玲,张新鹏.利用运动矢量进行视频篡改检测[J].计算机研究与发展,2009,46(z1):227-233.)
[4]SUBRAMANYAM A,EMMANUEL S.Video forgery detection using HOG features and compression properties[C]//Proceedings of the2012 14th International Workshop on Multimedia Signal Processing.Piscataway:IEEE,2012:89-94.
[5]CHEN Z,HUANG T,WU T,et al.Detection and recovery for copy-move forgery in homologous video[J].Computer Systems and Applications,2013,22(9):102-110.(陈智文,黄添强,吴铁浩,等.同源视频Copy-Move篡改检测及恢复[J].计算机系统应用,2013,22(9):102-110.)
[6]PENG F,NIE Y,LONG M.A complete passive blind image copymove forensics scheme based on compound statistics features[J].Forensic Science International,2011,212(1/2/3):21-25.
[7]WANG Z,BOVIK A C,SHEIKH H R,et al.Image quality assessment:from error visibility to structural similarity[J].IEEE Transactions on Image Processing,2004,13(4):600-612.
[8]QADIR G,YAHAYA S,HO A T.Surrey University Library for Forensic Analysis(SULFA)of video content[C]//Proceedings of the2012 IET Conference on Image Processing.London:IET Press,2012:1-6.
[9]DONG Q,YANG G,ZHU N.A MCEA based passive forensics scheme for detecting frame-based video tampering[J].Digital Investigation,2012,9(2):151-159.
[10]MILANI S,FONTANI M,BESTAGINI P,et al.An overview on video forensics[J].APSIPA Transactions on Signal and Information Processing,2012,1(1):1-18.
[11]SHANABLEH T.Detection of frame deletion for digital video forensics[J].Digital Investigation,2013,10(4):350-360.
[12]BIAN S,LUO W,HUANG J.Detecting video frame-rate up-conversion based on periodic properties of inter-frame similarity[J].Multimedia Tools and Applications,2013,2(3):1-15.
[13]KOBAYASHI M,OKABE T,SATO Y.Detecting forgery from static-scene video based on inconsistency in noise level functions[J].IEEE Transactions on Information Forensics and Security,2010,5(4):883-892.
[14]STAMM M C,LIN W S,LIU K J R.Temporal forensics and antiforensics for motion compensated video[J].IEEE Transactions on Information Forensics and Security,2012,7(4):1315-1329.
[15]CHEN R,DONG Q,REN H,et al.Video forgery detection based on non-subsampled Contourlet transform and gradient information[J].Information Technology Journal,2012,11(10):1456-1462.