Integrity Verification of the field sequence in Video Content
- 주제(키워드) Digital forensics , data structure , video forgery , integrity verification
- 발행기관 고려대학교 대학원
- 지도교수 이희조
- 발행년도 2017
- 학위수여년월 2017. 2
- 학위구분 석사
- 학과 대학원 컴퓨터·전파통신공학과
- 원문페이지 41 p
- 실제URI http://www.dcollection.net/handler/korea/000000071853
- 본문언어 영어
- 제출원본 000045897609
초록/요약
Video content stored in Video Event Data Recorders(VEDRs) are used as important evidence when certain events such as vehicle collisions occur. However, with sophisticated video editing software, assailants can easily manipulate video records to their advantage without leaving visible clues. Therefore, the integrity of video content recorded through VEDRs cannot be guaranteed, and the number of related forensic issues increases. Existing video integrity detection methods use the statistical properties of the pixels within each frame of the video. However, these methods require ample time, because they check frames individually. Moreover, the frame can easily be replaced and forged using the appropriate public software. To solve this problem, we propose an integrity checking mechanism using the structure of ordered fields in a video file, because existing video editing software does not allow users to access or modify field structures. In addition, because our proposed method involves checking the header information of video content only once, much less detection time is required compared with existing methods that examine the entire frames. We store an ordered file structure of video content as a signature in the database using a customized automated tool. The signature appears according to the video editing software. Then, the suspected video content is compared to a set of signatures. If the file structure matches with a signature, we recognize a manipulated video file by its corresponding editing software. We tested five types of video editing software that cover 99% of the video editing software market share. Furthermore, we arranged all saving options for each six video editing suites. As a result, we obtained 100% detection accuracy using stored signatures, without false positives, in a collection of 327,549 video files. The principle of this method can be applied to other video formats.
more목차
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
1 Introduction 1
2 Background 6
2.1 General AVI file format structure . . . . . . . . . . . . . . . . . 6
2.2 General MP4 file format structure . . . . . . . . . . . . . . . . 8
3 Video editing software 10
3.1 AVI rendering options . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 MP4 rendering options . . . . . . . . . . . . . . . . . . . . . . . 11
4 Automated signature collection method 13
5 Video editing software observation results 15
5.1 AVI observation . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
5.2 MP4 observation . . . . . . . . . . . . . . . . . . . . . . . . . . 19
6 Detection algorithm and evaluation 22
7 Summary and conclusion 25
References 28
Summary (in Korean) 31
