In recent years, video forensic investigation has become a prominent research area, due to the adverse effect of fake videos on networks, people and society. This paper summarizes all the existing methodologies used for forgery detection in H.265/HEVC videos. HEVC video forgery is generally classified into two categories as video quality forgery and video content forgery. The occurrence of various forgeries such as transcoding, fake-bitrate, inter-frame forgery and intra-frame forgery is deeply analyzed based on features extracted from the HEVC compression domain. The major findings of this research are (i) Less focus on transcoding detection, (ii) Non-availability of HEVC forged video dataset (iii) More focus on double compression detection for forgery detection, and (iv) Non-consideration of adaptive-GOP structure. The forgery detection in the video is critically important due to its wide use as the primary source of information in criminal investigations and proving the authenticity of contents. So, the forgery detection accuracy is of major concern at the present time. Although, various forgery detection methods are developed in past but the findings of this review point out the need of developing more effective detection methods with high accuracy.

Object tracking has always been a hotspot in the field of computer vision and has myriad applications in the real world. A major problem in this field is that of the successful tracking of a moving object undergoing occlusion in its path. This paper presents centroid based tracking scheme of a moving object without any apriori information of its shape or motion. Once the boundary of the object of interest is obtained, the centroid is calculated from its first order moments. This centroid is further utilized to detect the partial occlusion of test object by some other still or moving object in image frame. In case occlusion is detected, the new centroid location of moving object is predicted for subsequent video frames. The proposed algorithm is able to successfully detect moving object undergoing partial or total occlusion. Experimental results of our algorithm are compared with a popular tracking technique based on Mean Shift tracking algorithm.