Exposing digital forgeries in interlaced and deinterlaced video

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.