# Reliable Integrity Preservation Analysis of Video Contents with Support of Blockchain Systems
## 2 Related Works  
Due to device manufacturing costs, most legacy camera recording systems were not equipped with intentional authentication information such as an invisible watermark. Therefore, the passive blind approach without any authentication information has been more actively studied compared to the active intrusive approach with authentication information. The passive blind approach extracts descriptive features such camera sensor artifacts, coding artifacts and material object features from the video data, and it analyzes them to detect abnormal patterns of forged multimedia [5; 6]. Passive blind techniques are classified again into detection of inter-frame forgery and detection of intra-frame forgery [5]. In the inter-frame forgery, some frames are deleted, inserted or duplicated, while some pixel region within a frame is manipulated in the intra-frame forgery. Most of the previous studies [11] for the inter-frame forgery examine the artifacts of optical flow or motion vectors of objects, and those [12; 13; 14] for the intra-frame forgery examine the artifacts of noise residue or multiple compressions. All of these passive techniques determine the target video as tampered when the examined artifacts exceed a given threshold. However, it is very difficult to find a proper threshold, and an improper threshold may result in a lot of incorrect detections, such as True Negative detection and False Positive detection. Recently, deep-learning-based detection techniques [15; 16; 17] have been actively investigated to find out the tampered videos. These deep-learning-based methods do not require the threshold decision of abnormal patterns and automatically find out the suspicious static images in the target video with the forgery probability. However, this deep-learning approach requires the prelearning procedure with a well-defined training set. The performance of the deep-learning approach heavily depends on the accuracy of its pre-learning procedure. The recovery approach of deleted videos focuses on searching artifacts of deleted files upon unallocated storage are of file systems, when the deleted video files remain in the unallocated area until they are overwritten with new video files [18]. The searching technique is called _carying_[19], and it tries to find the unique header or the footer signature of deleted files. Recent carving methods [20; 21; 22; 23] considered the case that a deleted video file is split into multiple fragments and the fragments are spread in non-continuous locations. They search for spread fragments and combine them into a single file based on the information of fragment size and fragment location in the video file. The source authentication approach focuses on identifying the specific model of source digital devices or video editing tools. The traditional source authentication methods [1] examine the unique characteristics of digital video devices such as parameters of lens radial distortion and sensor imperfections in the artifacts of digital multimedia contents. Recent source authentication methods [8; 9; 24; 25; 26] examine software artifacts of video codec in the file metadata, instead of artifacts of digital multimedia contents. These studies consider both software characteristics of video editing tools and digital video cameras. If the extracted metadata are matched with the software characteristics of some camera device, the scheme determines that the given video file has integrity-guaranteed multimedia contents. On the other hand, if the extracted metadata are matched with software characteristics of some video editing tool, the scheme determines that the given video file has integrity-unguaranteed multimedia contents. This approach can quickly verify the name of the video editing tools or camera devices that last touched the target video file.