Manipulation detection in uncompressed video

In recent years due to advancement in video and image editing tools it has become increasingly easy to modify content of multimedia. Doctored videos are very difficult to identify through visual examination as artifacts left behind by processing steps are subtle and cannot be easily captured visually. Therefore, the integrity of digital videos cannot be taken for granted and these are not readily acceptable as a proof-of-evidence in a court-of-law. Hence, identifying the authenticity of videos has become an important field of information security. This thesis presents an approach to detect and temporally localize video forgery, based on the correlation of noise residual using the Discrete Wavelet Transformation (DWT) algorithm for de-noising. The proposed algorithm is tested on public datasets such as SULFA are used for performance evaluation. The results show that the approach is effective against manipulation techniques. In addition, it detects and localizes tampered frames in a video with high accuracy.