JPEG image width estimation for file carving

JPEG image width estimation for file carving

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Image width is an important factor for making the partially recovered data perceptually meaningful in image file carving. The authors conduct a comprehensive comparison of the performance of the representative methods for estimating the JPEG image width. Experimental results show that the best methods based on pixels are always better than the best methods based on quantised discrete cosine transform (DCT) coefficients. To keep the good performance of the pixel-based methods when the correct quantisation tables are unavailable, the authors replace the correct quantisation tables with the standard ones. Experimental results certify that such a replacement has only a little effect on the performance of the pixel-based methods, the best of which still outperform the best methods based on quantised DCT coefficients. The two results indicate that it may be enough to just focus on the pixel-based methods for future work. Finally, they propose a pixel-based method, which derives the candidate image widths from the most likely adjacent minimum coded unit (MCU) pairs in the vertical direction. The candidate width which appears most frequently is chosen as the estimated image width. Experimental results show that the proposed method usually has the best performance when most MCUs of an image are recovered.


    1. 1)
      • 1. Shanmugasundaram, K., Memon, N.: ‘Automatic reassembly of document fragments via data compression’. 2nd Digital Forensics Research Workshop, Syracuse, 2002.
    2. 2)
      • 2. Pal, A., Shanmugasundaram, K., Memon, N.: ‘Automated reassembly of fragmented images’. 2012 IEEE Int. Conf. on Multimedia and Expo, Baltimore, USA, 2003, vol. 1, pp. 625628.
    3. 3)
      • 3. Na, G.-H., Shim, K.-S., Moon, K.-W., et al: ‘Frame-based recovery of corrupted video files using video codec specifications’, IEEE Trans. Image Process., 2014, 23, (2), pp. 517526.
    4. 4)
      • 4. Memon, N., Pal, A.: ‘Automated reassembly of file fragmented images using greedy algorithms’, IEEE Trans. Image Process., 2006, 15, (2), pp. 385393.
    5. 5)
      • 5. Uzun, E., Sencar, H.T.: ‘Carving orphaned jpeg file fragments’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (8), pp. 15491563.
    6. 6)
      • 6. De Bock, J., De Smet, P.: ‘Jpgcarve: an advanced tool for automated recovery of fragmented jpeg files’, IEEE Trans. Inf. Forensics Sec., 2016, 11, (1), pp. 1934.
    7. 7)
      • 7. Garfinkel, S.L.: ‘Carving contiguous and fragmented files with fast object validation’, Digit. Invest., 2007, 4, pp. 212.
    8. 8)
      • 8. Cohen, M.I.: ‘Advanced jpeg carving’. Proc. of the 1st Int. Conf. on Forensic Applications and Techniques in Telecommunications, Information, and Multimedia and Workshop, Adelaide, Australia, 2008, p. 16.
    9. 9)
      • 9. Karresand, M., Shahmehri, N.: ‘Reassembly of fragmented jpeg images containing restart markers’. 2008 European Conf. on Computer Network Defense, Dublin, UK, 2008.
    10. 10)
      • 10. Pal, A., Sencar, H.T., Memon, N.: ‘Detecting file fragmentation point using sequential hypothesis testing’, Digit. Invest., 2008, 5, pp. S2S13.
    11. 11)
      • 11. Sencar, H.T., Memon, N.: ‘Identification and recovery of jpeg files with missing fragments’, Digit. Invest., 2009, 6, pp. S88S98.
    12. 12)
      • 12. Pal, A., Memon, N.: ‘The evolution of file carving’, IEEE Signal Process. Mag., 2009, 26, (2), pp. 5971.
    13. 13)
      • 13. Poisel, R., Tjoa, S., Tavolato, P.: ‘Advanced file carving approaches for multimedia files’, J. Wirel. Mob. Netw. Ubiquit. Comput. Dependable Appl., 2011, 2, (4), pp. 4258.
    14. 14)
      • 14. Abdullah, N.A., Ibrahim, R., Mohamad, K.M.: ‘Carving thumbnail/s and embedded jpeg files using image pattern matching’, J. Softw. Eng. Appl., 2013, 6, (03), p. 62.
    15. 15)
      • 15. Ying, H.-M., Thing, V.L.: ‘A novel inequality-based fragmented file carving technique’. Forensics in Telecommunications, Information, and Multimedia, Shanghai, China, 2011, pp. 2839.
    16. 16)
      • 16. Xu, M., Dong, S.: ‘Reassembling the fragmented jpeg images based on sequential pixel prediction’. Int. Symp. on Computer Network and Multimedia Technology, Wuhan, China, 2009, pp. 16.
    17. 17)
      • 17. Jarrett, H.M., Bailie, M.W., Hagen, E., et al: ‘Searching and seizing computers and obtaining electronic evidence in criminal investigations’ (Office of Legal Education, Executive Office for United States Attorneys, US Department of Justice, Computer Crime and Intellectual Property Section, 2009).
    18. 18)
      • 18. Everingham, M., Van Gool, L., Williams, C.K., et al: ‘The pascal visual object classes (voc) challenge’, Int. J. Comput. Vis., 2010, 88, (2), pp. 303338.
    19. 19)
      • 19. Xu, Y., Xu, M.: ‘Width extraction of jpeg fragment via frequency coefficients scale similarity measuring’. 2010 2nd Int. Conf. on Future Computer and Communication (ICFCC), Wuhan, China, 2010, pp. V2513.
    20. 20)
      • 20. Ayalneh, D.A., Choi, Y., Kim, H.J.: ‘Early width estimation of fragmented jpeg with corrupted header’, Multimedia Tools Appl., 2015, 75, (22), pp. 116.
    21. 21)
      • 21. I.T. Union: ‘Information technology-digital compression and coding of continuous-tone still images-requirements and guidelines’. Available at
    22. 22)
      • 22. Karresand, M., Shahmehri, N.: ‘Oscar ął file type identification of binary data in disk clusters and ram pages’, 2006, pp. 413424.
    23. 23)
      • 23. Roussev, V., Garfinkel, S.L.: ‘File fragment classification-the case for specialized approaches’. 2009 Fourth Int. IEEE Workshop on Systematic Approaches to Digital Forensic Engineering, Berkeley, USA, May 2009, pp. 314.
    24. 24)
      • 24. Calhoun, W.C., Coles, D.: ‘Predicting the types of file fragments’, Digit. Invest., 2008, 5, pp. S14S20.
    25. 25)
      • 25. Veenman, C.J.: ‘Statistical disk cluster classification for file carving’. Third Int. Symp. on Information Assurance and Security, Manchester, UK, 2007, pp. 393398.
    26. 26)
      • 26. Sportiello, L., Zanero, S.: ‘File block classification by support vector machine’. 2011 Sixth Int. Conf. on Availability, Reliability and Security (ARES), Vienna, Austria, 2011, pp. 307312.
    27. 27)
      • 27. S., P.N., T., V.L.L., Li, Q., et al: ‘A novel support vector machine approach to high entropy data fragment classification’. Proc. of the South African Information Security Multi-Conf. (SAISMC 2010), Port Elizabeth, South Africa, 2010, pp. 236247.

Related content

This is a required field
Please enter a valid email address