Automating Video File Carving and Content Identification

The massive amount of illegal content, especially images and videos, encountered in forensic investigations requires the development of tools that can automatically recover and analyze multimedia data from seized storage devices. However, most forensic analysis processes are still done manually or require continuous human interaction. The identification of illegal content is particularly time consuming because no reliable tools for automatic content classification are currently available. Additionally, multimedia file carvers are often not robust enough – recovering single frames of video files is often not possible if some of the data is corrupted or missing. This paper proposes the combination of two forensic techniques – video file carving and robust hashing – in a single procedure that can be used for the automated recovery and identification of video content, significantly speeding up forensic investigations.

[1]  Paul Douglas,et al.  International Conference on Information Technology : Coding and Computing , 2003 .

[2]  Nasir D. Memon,et al.  Automated reassembly of file fragmented images using greedy algorithms , 2006, IEEE Transactions on Image Processing.

[3]  Marcus K. Rogers,et al.  Computer Forensics Field Triage Process Model , 2006, J. Digit. Forensics Secur. Law.

[4]  Jiri Fridrich,et al.  Robust hash functions for digital watermarking , 2000, Proceedings International Conference on Information Technology: Coding and Computing (Cat. No.PR00540).

[5]  Jaap A. Haitsma,et al.  Robust Audio Hashing for Content Identification , 2001 .

[6]  Marcel Worring,et al.  Proceedings of the First ACM workshop on Multimedia in forensics, MiFor '09, Beijing, China, October 23, 2009 , 2009, MiFor@MM.

[7]  Sangjin Lee,et al.  A study on multimedia file carving method , 2011, Multimedia Tools and Applications.

[8]  Simson L. Garfinkel,et al.  Using purpose-built functions and block hashes to enable small block and sub-file forensics , 2010, Digit. Investig..

[9]  Simson L. Garfinkel,et al.  Carving contiguous and fragmented files with fast object validation , 2007, Digit. Investig..

[10]  Xuebing Zhou,et al.  Video Perceptual Hashing Using Interframe Similarity , 2006, Sicherheit.

[11]  Husrev T. Sencar,et al.  Detecting file fragmentation point using sequential hypothesis testing , 2008, Digit. Investig..

[12]  Bian Yang,et al.  Block Mean Value Based Image Perceptual Hashing , 2006, 2006 International Conference on Intelligent Information Hiding and Multimedia.

[13]  Martin Steinebach,et al.  ForBild: efficient robust image hashing , 2012, Other Conferences.

[14]  Eric Allamanche,et al.  Content-based Identification of Audio Material Using MPEG-7 Low Level Description , 2001, ISMIR.

[15]  Golden G. Richard,et al.  Scalpel: A Frugal, High Performance File Carver , 2005, DFRWS.