• DocumentCode
    3577049
  • Title

    A method to identify the AVI-type blocks based on their four-character codes and C4.5 algorithm

  • Author

    Jun Pan ; Liying Liu ; Guozi Sun ; Yanbin Tang

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Nanjing Univ. of Posts & Telecommun., Nanjing, China
  • fYear
    2014
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    Video file carving is crucial to computer forensics and very challenging for researchers. Identification of related data plays a key role in video file carving. Most researches on this topic rely on statistical analysis by establishing a machine learning model. However, they didn´t show good performance on video data. It is almost impossible to give a general method to classify all kinds of file blocks due to different characteristics between file blocks. In this paper, we focus on AVI files. AVI files are one of widely used video format, especially in camera. We describe the framework of video file carving and propose a method to identify AVI-type blocks for a range of relevant file block types, which is based on the four-character codes of the structure of AVI and applies the C4.5 algorithm. The experiments achieve a high true positive rate after these blocks are classified twice. In addition, we also make an outlook for subsequent work in video file carving.
  • Keywords
    decision trees; digital forensics; learning (artificial intelligence); multimedia systems; statistical analysis; video streaming; AVI files; AVI-type block; C4.5 algorithm; computer forensics; four-character codes; machine learning; statistical analysis; video file carving; Classification algorithms; Codecs; Decision trees; Encoding; Forensics; Head; Training; C4.5 Decision Tree Algorithm; Classification; Four-character Codes; Video File carving;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Behavior, Economic and Social Computing (BESC), 2014 International Conference on
  • Type

    conf

  • DOI
    10.1109/BESC.2014.7059521
  • Filename
    7059521