A new approach for JPEG resize and image splicing detection

1st ACM Workshop on Multimedia in Forensics - MiFor'09, Co-located with the 2009 ACM International Conference on Multimedia, MM'09

Volumn , Issue , 2009, Pages 43-47

  Liu, Qingzhong ^a   Sung, Andrew H ^a  

^a NEW MEXICO INSTITUTE OF MINING AND TECHNOLOGY   (United States)

Author keywords

Forgery detection; Generalized gaussian distribution; Image resize; Joint density; JPEG; Splicing; SVM

Indexed keywords

DCT COEFFICIENTS; DETECTION PERFORMANCE; DIGITAL IMAGE; DIGITAL MEDIA; DISCRETE COSINE TRANSFORM COEFFICIENTS; GENERALIZED GAUSSIAN DISTRIBUTION; GENERALIZED GAUSSIAN DISTRIBUTIONS; IMAGE COMPLEXITY; IMAGE FORGERY; IMAGE SPLICING DETECTION; JOINT DENSITIES; JPEG IMAGE; NEW APPROACHES; SCALE FACTOR; SHAPE PARAMETERS;

COSINE TRANSFORMS; DIGITAL STORAGE; DISCRETE COSINE TRANSFORMS; GAUSSIAN DISTRIBUTION; METHOD OF MOMENTS; MULTIMEDIA SYSTEMS; SUPPORT VECTOR MACHINES;

IMAGE CODING;

EID: 72249118627     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1631081.1631092     Document Type: Conference Paper

References (19)

1. Chen W, Shi YQ and Su W (2007). Image splicing detection using 2-D phase congruency and statistical moments of characteristic function. Proc. SPIE, Vol. 6505, 65050R (2007); DOI:10.1117/12.704321.
2. Cho D and Bui T (2005). Multivariate statistical modeling for image denoising using wavelet transforms. Signal Processing: Image Communication, 20:77-89.
3. Farid H (1999). Detecting digital forgeries using bispectral analysis. AI Lab, Massachusetts Institute of Technology, Tech. Rep. AIM-1657, 1999.
4. Farid H (2009). Image forgery detection, a survey. IEEE Signal Processing Magazine, March 2009, 16-25.
5. nd Canadian Conf. Computer and Robot Vision, vol. 171, pp. 65-72.
6. Hsu YF and Chang SF (2007). Image splicing detection using camera response function consistency and automatic segmentation. Proc. 2007 IEEE International Conference on Multimedia and Expo, pp. 28-31.
7. th ACM Multimedia and Security Workshop, pp. 11-20.
8. th International Joint Conference on Artificial Intelligence, pp. 2808-2813, 2007.
9. Liu Q, Sung AH, Chen Z and Xu J (2008). Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images. Pattern Recognition, 41(1): 56-66.
10. Liu Q, Sung AH, Ribeiro B, Wei M, Chen Z and Xu J (2008). Image complexity and feature mining for steganalysis of least significant bit matching steganography. Information Sciences, 178(1): 21-36.
11. Liu Q, Sung AH, and Qiao M (2009). Improved detection and evaluation for JPEG steganalysis. ACM-MM 2009, Bejing, China. October 19-24, 2009.
12. Mahdian B and Saic S (2008). Blind authentication using periodic properties of interpolation. IEEE Trans. Inform. Forensics Security 3(3): 529-538.
13. Ng TT and Chang SF (2004). A model for image splicing. Proc. 2004 International Conference on Image Processing, vol. 2, pp. 1169-1172.
14. Ng TT and Chang SF (2004). A data set of authentic and spliced image blocks, ADVENT Technical Report #203-2004-3 Columbia University, June 2004. Available at: http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataS et/AuthSplicedDataSet.htm
15. Popescu AC and Farid H (2005). Exposing digital forgeries by detecting traces of re-sampling. IEEE Trans. Signal Processing 53(2): 758-767.
16. Prasad S and Ramakrishnan KR (2006). On resampling detection and its application to image tampering. Proc. IEEE Int. Conf. Multimedia and Exposition 2006, pp. 1325-1328.
17. Sharifi K and Garcia AL (1995). Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video. IEEE Trans. Circuits Syst. Video Technol. 5: 52-56.
18. th workshop on Multimedia & Security, pp. 51-62.
19. Vapnik VN (1998). Statistical Learning Theory. John Wiley and Sons, New York.