The methodology adopted for the survey. 1. Data Acquisition: Articles have been collected from google scholar with the help of keywords video forensics, video forgery detection, inter-frame forgery, object forgery, digital forensics etc. A total number of 352 articles have been collected. 2. Article Screening: An elimination approach based on the type of data used for forgery detection has been implemented in order to obtain the relevant articles. The collected research articles contain various articles performing forensic investigations on image data. Such articles are removed and articles with video data are retained. Further, articles investigating compression domain features of the HEVC codec are identified. 3. Categorization: The selected articles are categorized as per the general objective of forgery detection as transcoding detection, fake-bitrate detection, inter-frame forgery detection and intra-frame forgery detection. 4. Article Analysis: Finally, the working concept, model used, datasets, and the performance measures of existing methodologies are examined and analysed, in order to get depth knowledge of this field. As discussed, any type of forgery operation requires the video sequence to be recompressed, which inevitably leaves artefacts in the fundamental characteristics of coding elements. Such artefacts have been actively investigated by several authors in the past. The Venn diagram in Fig. 11 shows the distributions of articles based on the coding unit utilized for forensic feature extraction. The objective of this section is to provide a comprehensive review of the techniques proposed in the literature for detecting various forgeries in HEVC-coded videos using compression domain features. The detection techniques are grouped into three categories: transcoding detection, fake bitrate detection, and double compression detection. The pie chart in Fig. 12 graphically depicts the proportion of articles published by various researchers among these three categories.