Passive copy-move forgery detection in videos

Due to evolution of prevalent sophisticated digital video editing software it has become easier to forge any video. Copy-Move forgery is a special kind of video forgery technique, in which copy-move of the frame region is performed in intra frame or inter frames. This copy-move introduces a special kind of relationship between the original frame segment and the pasted one, which provides clue for copy-move forgery detection (CMFD). In this paper a passive forensic scheme has been considered for CMFD in spatial and temporal domain of video. For detecting copy-move forgery in spatial domain, Scale Invariant Features Transform (SIFT) is proposed. Noise residue and Correlation technique have been proposed for temporal copy-move forgery detection. Copy-Move forgery detection using SIFT features show robust result in comparison to other features because SIFT features are immune to various type of transformation like scaling, translation, rotation etc. Experimental results show that our technique successfully detect spatial and temporal copy-move forgery in the video.

[1]  Alberto Del Bimbo,et al.  Ieee Transactions on Information Forensics and Security 1 a Sift-based Forensic Method for Copy-move Attack Detection and Transformation Recovery , 2022 .

[2]  Sabu Emmanuel,et al.  Video forgery detection using HOG features and compression properties , 2012, 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP).

[3]  Nenghai Yu,et al.  Rotation robust detection of copy-move forgery , 2010, 2010 IEEE International Conference on Image Processing.

[4]  Weihong Wang,et al.  Exposing Digital Forgeries in Interlaced and Deinterlaced Video , 2007, IEEE Transactions on Information Forensics and Security.

[5]  Mohan S. Kankanhalli,et al.  A Survey on Digital Camera Image Forensic Methods , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[6]  Chandrika Kamath,et al.  Denoising through wavelet shrinkage: an empirical study , 2003, J. Electronic Imaging.

[7]  Mohan S. Kankanhalli,et al.  A scalable signature scheme for video authentication , 2006, Multimedia Tools and Applications.

[8]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[9]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[10]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Weihong Wang,et al.  Exposing digital forgeries in video by detecting double quantization , 2009, MM&Sec '09.

[12]  Chia-Wen Lin,et al.  Video forgery detection using correlation of noise residue , 2008, 2008 IEEE 10th Workshop on Multimedia Signal Processing.

[13]  Xunyu Pan,et al.  Region Duplication Detection Using Image Feature Matching , 2010, IEEE Transactions on Information Forensics and Security.

[14]  Takahiro Okabe,et al.  Detecting Video Forgeries Based on Noise Characteristics , 2009, PSIVT.

[15]  Anthony T. S. Ho,et al.  Surrey University Library for Forensic Analysis (SULFA) of video content , 2012 .

[16]  Paolo Bestagini,et al.  Local tampering detection in video sequences , 2013, 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP).

[17]  Weihong Wang,et al.  Exposing digital forgeries in video by detecting duplication , 2007, MM&Sec.