Exposing digital forgeries in video by detecting duplication
Proceedings of the 9th workshop on Multimedia & security
Information Hiding
Detecting Video Forgeries Based on Noise Characteristics
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Exposing digital forgeries in video by detecting double quantization
Proceedings of the 11th ACM workshop on Multimedia and security
Design and deployment of a digital forensics service platform for online videos
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Exposing digital video forgery by ghost shadow artifact
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Screenshot identification using combing artifact from interlaced video
Proceedings of the 12th ACM workshop on Multimedia and security
Detecting forgery from static-scene video based on inconsistency in noise level functions
IEEE Transactions on Information Forensics and Security
Vision of the unseen: Current trends and challenges in digital image and video forensics
ACM Computing Surveys (CSUR)
Novel blind video forgery detection using markov models on motion residue
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Detecting removed object from video with stationary background
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
A novel video inter-frame forgery model detection scheme based on optical flow consistency
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
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With the advent of high-quality digital video cameras and sophisticated video editing software, it is becoming increasingly easier to tamper with digital video. A growing number of video surveillance cameras are also giving rise to an enormous amount of video data. The ability to ensure the integrity and authenticity of these data poses considerable challenges. We describe two techniques for detecting traces of tampering in deinterlaced and interlaced video. For deinterlaced video, we quantify the correlations introduced by the camera or software deinterlacing algorithms and show how tampering can disturb these correlations. For interlaced video, we show that the motion between fields of a single frame and across fields of neighboring frames should be equal. We propose an efficient way to measure these motions and show how tampering can disturb this relationship.