Multiple classifier systems for image forgery detection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8157 LNCS, Issue PART 2, 2013, Pages 259-268

  Cozzolino, Davide ^a   Gargiulo, Francesco ^a   Sansone, Carlo ^a   Verdoliva, Luisa ^a  

^a UNIVERSITY OF NAPLES FEDERICO II   (Italy)

Author keywords

digital forensics; Forgery detection; image tampering

Indexed keywords

ABSTRACT LEVELS; DIGITAL FORENSIC; FORGERY DETECTIONS; IMAGE FORGERY DETECTIONS; IMAGE TAMPERING; MULTIPLE CLASSIFIER SYSTEMS; PROCESSING TOOLS; SOURCES OF INFORMATIONS;

IMAGE ANALYSIS;

DETECTORS;

EID: 84884706379     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-41184-7_27     Document Type: Conference Paper

References (17)

1. Barni, M., Costanzo, A.: A fuzzy approach to deal with uncertainty in image forensics. Signal Processing: Image Communication 27(9), 998-1010 (2012)
2. Bianchi, T., De Rosa, A., Piva, A.: Improved dct coefficient analysis for forgery localization in jpeg images. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2444-2447 (May 2011)
3. Chierchia, G., Parrilli, S., Poggi, G., Sansone, C., Verdoliva, L.: On the influence of denoising in PRNU based forgery detection. In: ACM Workshop on Multimedia in Forensics, Security and Intelligence, Firenze, Italy, pp. 117-122 (2010)
4. Cozzolino, D., Poggi, G., Sansone, C., Verdoliva, L.: A comparative analysis of forgery detection algorithms. In: Gimel'farb, G., Hancock, E., Imiya, A., Kuijper, A., Kudo, M., Omachi, S., Windeatt, T., Yamada, K. (eds.) SSPR&SPR 2012. LNCS, vol. 7626, pp. 693-700. Springer, Heidelberg (2012)
5. Farid, H.: Exposing Digital Forgeries From JPEG Ghosts. IEEE Transactions on Information Forensics and Security 4(1), 154-160 (2009)
6. Fontani, M., Bianchi, T., De Rosa, A., Piva, A., Barni, M.: A dempster-shafer framework for decision fusion in image forensics. In: IEEE International Workshop on Information Forensics and Security (WIFS), November 29-December 2, pp. 1-6 (2011)
7. Gallagher, A.: Detection of linear and cubic interpolation in jpeg compressed images. In: The 2nd Canadian Conference on Computer and Robot Vision, pp. 65-72 (May 2005)
8. Gordon, J., Shortliffe, E.: The dempster-shafer theory of evidence. In: Buchanan, B.G., Shortliffe, E. (eds.) Rule-Based Expert Systems, pp. 272-292 (1984)
9. Hsu, Y.F., Chang, S.F.: Statistical fusion of multiple cues for image tampering detection. In: Asilomar Conference on Signals, Systems, and Computers (2008)
10. Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226-239 (1998)
11. Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience (2004)
12. Li, W., Yuan, Y., Yu, N.: Passive detection of doctored JPEG image via block artifact grid extraction. Signal Processing 89(9), 1821-1829 (2009)
13. Lukas, J., Fridrich, J., Goljan, M.: Detecting digital image forgeries using sensor pattern noise. In: Proceedings of the SPIE, vol. 6072, SPIE (2006)
14. Mahdian, B., Saic, S.: Blind Authentication Using Periodic Properties of Interpolation. IEEE Transactions on Information Forensics and Security 3(3), 529-538 (2008)
15. Piva, A.: An Overview on Image Forensics. ISRN Signal Processing pp. 1-22 (2012)
16. Schaefer, J., Stich, M.: Ucid - an uncompressed colour image database. In: Proc. SPIE, Storage and Retrieval Methods and Applications for Multimedia, pp. 472-480 (2004)
17. Xu, L., Krzyzak, A., Suen, C.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man, and Cybernetics 22(3), 418-435 (1992)