Digital media has numerous benefits and has changed our lives for the better in many ways. There is however a growing concern for the authenticity of the content being shared. This project aims at judging the authenticity of video data by the detection of video forgery. In recent years intelligent systems like faceswap, deepfakes and Face2Face, have been used to create fake content that seems very realistic and very hard to detect. Video forgery in general scenarios leaves certain inconsistencies such as optical flow inconsistencies, jagged edges, or ill shaped objects, that can be detected by steganographic techniques and deep networks. This project employs statistical and physiological analysis for the detection of video forgery.