ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Exposing digital forgeries in video by detecting duplication
Proceedings of the 9th workshop on Multimedia & security
Exposing digital forgeries in video by detecting double quantization
Proceedings of the 11th ACM workshop on Multimedia and security
Exposing digital video forgery by ghost shadow artifact
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
A Markov process based approach to effective attacking JPEG steganography
IH'06 Proceedings of the 8th international conference on Information hiding
Detecting forgery from static-scene video based on inconsistency in noise level functions
IEEE Transactions on Information Forensics and Security
Exposing Digital Forgeries in Interlaced and Deinterlaced Video
IEEE Transactions on Information Forensics and Security - Part 1
Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting
IEEE Transactions on Image Processing
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In this paper we present a novel blind video forgery detection method by applying Markov models to motion in videos. Motion is an important aspect of video forgery detection as it effects forgery detection in videos. Most of the current video forgery detection algorithms do not consider motion in their approach. Motion is usually captured from motion vectors and prediction error frame. However capturing motion for I-frame is computationally expensive, so in this paper we extract the motion information by applying collusion on successive frames. First a base frame is obtained by applying collusion on successive frames and the difference between actual and estimate gives information about motion. Then we apply Markov models on this motion residue and apply pattern recognition on this. We used Support Vector Machines (SVMs) in our experiment. We obtained an accuracy of 87% even for reduced feature set.