基于TCS-LBP算子的视频帧复制篡改检测算法
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摘要
针对监控视频帧复制篡改,提出一种基于时空域特征的篡改检测算法.受空域局部二值模式(LBP)算子设计的启发,设计一种时域TCS-LBP算子,反映当前视频帧与其前后若干帧在同一空间位置像素之间的关系;然后在当前图像上逐点计算TCS-LBP特征值,构造能同时反映当前视频帧时域和空域特征的特征图像;利用特征图像,逐帧检测是否存在帧复制;对于初步检测到的篡改区域,再进行虚警和漏检的修正以及篡改边界的精确定位.实验结果表明,文中算法具有良好的性能,与现有的2种同类算法相比,性能明显提升.
A spatial and temporal feature-based detection algorithm is proposed for surveillance video duplication detection. Motivated by the design of LBP(Local Binary Patterns) operator,we design a temporal domain CenterSymmetric Local Binary Pattern(TCS-LBP) operator which can reflect the relationship between the pixel on the current frame and those pixels on its front and back frames in the same spatial location. By calculating the TCS-LBP value pixel-by-pixel on the current frame,we construct its correspondent feature image which can reflect both spatial and temporal features of the current frame. The feature image is thus used for detecting video duplication frameby-frame. For the obtained results,we propose schemes for false alarms removal and missing detection correction.Finally,we accurately determine the boundaries of forgery region. Experimental results demonstrate that the proposed algorithm has good performance. It well outperforms the two similar algorithms in the literature.
A spatial and temporal feature-based detection algorithm is proposed for surveillance video duplication detection. Motivated by the design of LBP(Local Binary Patterns) operator,we design a temporal domain CenterSymmetric Local Binary Pattern(TCS-LBP) operator which can reflect the relationship between the pixel on the current frame and those pixels on its front and back frames in the same spatial location. By calculating the TCS-LBP value pixel-by-pixel on the current frame,we construct its correspondent feature image which can reflect both spatial and temporal features of the current frame. The feature image is thus used for detecting video duplication frameby-frame. For the obtained results,we propose schemes for false alarms removal and missing detection correction.Finally,we accurately determine the boundaries of forgery region. Experimental results demonstrate that the proposed algorithm has good performance. It well outperforms the two similar algorithms in the literature.
引文
[1] WANG W,FARID H. Exposing digital forgeries in video by detecting double MPEG compression[C]∥Proceedings of the 8th Work-shop on Multimedia and Security.Geneva:ACM,2006:37-47.
[2] WANG W,FARID H. Exposing digital forgeries in video by detecting double quantization[C]∥Proceedings of the11th ACM workshop on Multimedia and security. Princeton:ACM,2009:39-48.
[3] LIAO D,YANG R,LIU H. Double H. 264/AVC compression detection using quantized nonzero AC coefficients[J]. Proceedings of SPIE-The International Society for Optical Engineering,2011,7880(2):78800Q/1-10.
[4] HSU C C,HUANG T Y,LIN C W,et al. Video forgery detection using correlation of noise residue[C]∥Proceedings of 2008 IEEE 10th Workshop on Multimedia Signal Processing. Cairns:IEEE,2008:170-174.
[5] 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.
[6] BESTAGINI P,MILANI S,TAGLIASACCHI M,et al. Local tampering detection in video sequences[C]∥Proceedings of IEEE 15th International Workshop Multimedia Signal Processing(MMSP). Pula:IEEE,2013:488-493.
[7] CHEN S,TAN S,LI B,et al. Automatic detection of object-based forgery in advanced video[J]. IEEE Transactions on Circuits&Systems for Video Technology,2016,26(11):2138-2151.
[8] YANG J,HUANG T,SU L. Using similarity analysis to detect frame duplication forgery in videos[J]. Multimedia Tools and Applications,2016,75(4):1-19.
[9] BIDOKHTI A,GHAEMMAGHAMI S. Detection of regional copy/move forgery in MPEG videos using optical flow[C]∥Proceedings of International Symposium on Artificial Intelligence and Signal Processing.[S. l.]:IEEE,2015.
[10] WU Y,JIANG X,SUN T,et al. Exposing video interframe forgery based on velocity field consistency[C]∥Proceedings of ICASSP,IEEE International Conference on Acoustics,Speech and Signal Processing. Florence:IEEE,2014:2674-2678.
[11] D'AMIANO L,COZZOLINO D,POGGI G,et al. Video forgery detection and localization based on 3D patchmatch[C]∥Proceedings of IEEE International Conference on Multimedia&Expo Workshops.[S. l.]:IEEE,2015:1-6.
[12] WANG W,FARID H. Exposing digital forgeries in video by detecting duplication[C]∥Proceedings of the 9th Workshop on Multimedia&Security. Dallas:ACM,2007:35-42.
[13] 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):1250017.
[14] LI F,HUANG T. Video copy-move forgery detection and localization based on structural similarity[C]∥Proceedings of the 3rd International Conference on Multimedia Technology(ICMT 2013). Guangzhou:[s. n.],2014:63-76.
[15] 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.
[16] HEIKKILM,PIETIKINEN M,SCHMID C. Description of interest regions with local binary patterns[J].Pattern Recognition,2009,42(3):425-436.
[17] OJALA T,PIETIKINEN M,MENPT. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
[2] WANG W,FARID H. Exposing digital forgeries in video by detecting double quantization[C]∥Proceedings of the11th ACM workshop on Multimedia and security. Princeton:ACM,2009:39-48.
[3] LIAO D,YANG R,LIU H. Double H. 264/AVC compression detection using quantized nonzero AC coefficients[J]. Proceedings of SPIE-The International Society for Optical Engineering,2011,7880(2):78800Q/1-10.
[4] HSU C C,HUANG T Y,LIN C W,et al. Video forgery detection using correlation of noise residue[C]∥Proceedings of 2008 IEEE 10th Workshop on Multimedia Signal Processing. Cairns:IEEE,2008:170-174.
[5] 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.
[6] BESTAGINI P,MILANI S,TAGLIASACCHI M,et al. Local tampering detection in video sequences[C]∥Proceedings of IEEE 15th International Workshop Multimedia Signal Processing(MMSP). Pula:IEEE,2013:488-493.
[7] CHEN S,TAN S,LI B,et al. Automatic detection of object-based forgery in advanced video[J]. IEEE Transactions on Circuits&Systems for Video Technology,2016,26(11):2138-2151.
[8] YANG J,HUANG T,SU L. Using similarity analysis to detect frame duplication forgery in videos[J]. Multimedia Tools and Applications,2016,75(4):1-19.
[9] BIDOKHTI A,GHAEMMAGHAMI S. Detection of regional copy/move forgery in MPEG videos using optical flow[C]∥Proceedings of International Symposium on Artificial Intelligence and Signal Processing.[S. l.]:IEEE,2015.
[10] WU Y,JIANG X,SUN T,et al. Exposing video interframe forgery based on velocity field consistency[C]∥Proceedings of ICASSP,IEEE International Conference on Acoustics,Speech and Signal Processing. Florence:IEEE,2014:2674-2678.
[11] D'AMIANO L,COZZOLINO D,POGGI G,et al. Video forgery detection and localization based on 3D patchmatch[C]∥Proceedings of IEEE International Conference on Multimedia&Expo Workshops.[S. l.]:IEEE,2015:1-6.
[12] WANG W,FARID H. Exposing digital forgeries in video by detecting duplication[C]∥Proceedings of the 9th Workshop on Multimedia&Security. Dallas:ACM,2007:35-42.
[13] 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):1250017.
[14] LI F,HUANG T. Video copy-move forgery detection and localization based on structural similarity[C]∥Proceedings of the 3rd International Conference on Multimedia Technology(ICMT 2013). Guangzhou:[s. n.],2014:63-76.
[15] 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.
[16] HEIKKILM,PIETIKINEN M,SCHMID C. Description of interest regions with local binary patterns[J].Pattern Recognition,2009,42(3):425-436.
[17] OJALA T,PIETIKINEN M,MENPT. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.