EXIF-white balance recognition for image forensic analysis

Article

Abstract

Due to the lack of post-processing resistance, traditional forensic methods are vulnerable to cascade image manipulations, e.g. copy-and-paste operation followed by high compression. Different from these traditional methods, a new forensic method that has the ability to resist multiple types of post-processing, is proposed by using white balance from the EXchangeable Image File format (EXIF) header. We first extract image quality metrics between each two combination of one original image and twelve re-balanced images. By regularizing the eigen spectrum of image quality metrics, the compact set of image eigen features is then selected for recognizing different EXIF-white balance modes via the SVM classifier. The experimental results show that the proposed method has the ability to resist the influence of high compression or heavy downsampling in both theoretical and realistic scenarios. Furthermore, thanks to image eigen features affected by cascade image operations, it is possible to lead to a wrong white balance mode. Thus, we use the EXIF-white balance parameter as a manipulator indicator for forgery detection. Based on the forgery photos in practice, the proposed evidence can detect cascade manipulated images which are subject to copy-and-paste followed by different white balance post-processing operations, high compression or heavy downsampling.

Keywords

EXIF White balance Copy-and-paste Compression Resize  Downsampling Digital still camera  Forensic Manipulation detection 

References

  1. Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., & Serra, G. (2011). A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Transactions on Information Forensics and Security, 6(3), 1099–1110.CrossRefGoogle Scholar
  2. Avcibas, I., Bayram, S., Memon, N.D., Ramkumar, M., & Sankur, B. (2004). A classifier design for detecting image manipulations. In Proc. of ICIP, pp. 2645–2648.Google Scholar
  3. Avcibas, I., Memon, N., & Sankur, B. (2003). Steganalysis using image quality metrics. IEEE Transactions on Image Processing, 12, 221–229.MathSciNetCrossRefGoogle Scholar
  4. Bayram, S., Avcibas, I., Sankur, B., & Memon, N. D. (2006). Image manipulation detection. Journal Electronic Imaging, 15(4), 041,102.CrossRefGoogle Scholar
  5. Cao, H., & Kot, A. C. (2009). Accurate detection of demosaicing regularity for digital image forensics. IEEE Transactions on Information Forensics and Security, 4(4), 899–910.CrossRefGoogle Scholar
  6. Cao, H., & Kot, A. C. (2011). Detection of tampering inconsistencies on mobile photos. Digital Watermarking, Lecture Notes in Computer Science, 6526, 105–119.CrossRefGoogle Scholar
  7. Cao, J., & Lin, Z. (2014). Bayesian signal detection with compressed measurements. Information Sciences, 289, 241–253.CrossRefMATHGoogle Scholar
  8. Cao, J., & Lin, Z. (2015). Extreme learning machines on high dimensional and large data applications: A survey. Mathematical Problems in Engineering, 501, 1–12.Google Scholar
  9. Cao, J., Lin, Z., Huang, G., & Liu, N. (2012). Voting based extreme learning machine. Information Sciences, 185(1), 66–77.MathSciNetCrossRefGoogle Scholar
  10. Cao, J., & Xiong, L. (2014). Protein sequence classification with improved extreme learning machine algorithms. In BioMed Research International, 2014. Hindawi Publishing Corporation.Google Scholar
  11. Cao, J., Zhao, Y., Lai, X., Ong, M. E. H., Yin, C., Koh, Z. X., et al. (2015). Landmark recognition with sparse representation classification and extreme learning machine. Journal of the Franklin Institute, 352(10), 4528–4545.MathSciNetCrossRefGoogle Scholar
  12. Chen, M., Fridrich, J., Goljan, M., & Lukás, J. (2008). Determining image origin and integrity using sensor noise. IEEE Transactions on Information Forensics and Security, 3, 74–90.CrossRefGoogle Scholar
  13. Deng, Z., Gijsenij, A., & Zhang, J. (2011). Source camera identification using auto-white balance approximation. In ICCV, pp. 57–64.Google Scholar
  14. Eskicioglu, A., & Fisher, P. (1995). Image quality measures and their performance. IEEE Transactions on Communications, 43(12), 2959–2965.CrossRefGoogle Scholar
  15. Fan, J., Kot, A., Cao, H., & Sattar, F. (2011). Modeling the exif-image correlation for image manipulation detection. In Proc. of ICIP.Google Scholar
  16. Fan, J., Cao, H., & Kot, A. C. (2013). Estimating exif parameters based on noise features for image manipulation detection. IEEE Transactions on Information Forensics and Security, 8(4), 608–618.CrossRefGoogle Scholar
  17. Farid, H. (2009). A survey of image forgery detection. IEEE Signal Processing Magazine, 2(26), 16–25.CrossRefGoogle Scholar
  18. Finlayson, G., & Trezzi, E. (2004). Shades of gray and colour constancy. In Proc. IS and T/SID 12th Color Imaging Conf., pp. 37–41.Google Scholar
  19. Finlayson, G.D., Drew, M.S., & Funt, B.V. (1993). Diagonal transforms suffice for color constancy. In ICCV, pp. 164–171.Google Scholar
  20. Frese, T., Bouman, C., & Allebach, J.P. (1997). Methodology for designing image similarity metrics based on human visual system models. In Proc. SPIE Conference on Human Vision and Electronic Imaging II, Vol. 3016, pp. 472–483.Google Scholar
  21. Gao, Q., Huang, Y., Zhang, H., Hong, X., Li, K., & Wang, Y. (2015). Discriminative sparsity preserving projections for image recognition. Pattern Recognition, 48(8), 2543–2553.CrossRefGoogle Scholar
  22. Goljan, M., & Fridrich, J. (2008). Camera identification from cropped and scaled images. In Proc. SPIE Electronic Imaging, Forensics, Security, Steganography, and Watermarking of Multimedia Contents X, Vol. 6819, pp. 0E-1–0E-13.Google Scholar
  23. Gou, H., Swaminathan, A., & Wu, M. (2009). Intrinsic sensor noise features for forensic analysis on scanners and scanned images. IEEE Transactions on Information Forensics and Security, 4, 476–491.CrossRefGoogle Scholar
  24. He, J., Lin, Z., Wang, L., & Tang, X. (2006). Detecting doctored jpeg images via dct coefficient analysis. In ECCV (3), pp. 423–435.Google Scholar
  25. Ho, S. S., Dai, P., & Rudzicz, F. (2015). Manifold learning for multivariate variable-length sequences with an application to similarity search. IEEE Transactions on Neural Networks and Learning Systems, 1, 99.Google Scholar
  26. Jiang, X., Mandal, B., & Kot, A. C. (2008). Eigenfeature regularization and extraction in face recognition. IEEE Transactions Pattern Analysis Machine Intelligence, 30(3), 383–394.CrossRefGoogle Scholar
  27. Johnson, M. K., & Farid, H. (2008). Detecting photographic composites of people. In Yun Q. Shi, H.-J. Kim, S. Katzenbeisser (Eds.), Digital Watermarking. Lecture Notes in Computer Science (pp. 19–33). Guangzhou, China: Springer.Google Scholar
  28. Kee, E., Johnson, M. K., & Farid, H. (2011). Digital image authentication from jpeg headers. IEEE Transactions on Information Forensics and Security, 6(3), 1066–1075.CrossRefGoogle Scholar
  29. Lindbloom, B. (2007). Chromatic adaptation. Bruce J Lindbloom, Tech Rep.Google Scholar
  30. Lukáš, J., Fridrich, J., & Goljan, M. (2006). Detecting digital image forgeries using sensor pattern noise. In Proc of the SPIE Computer Engineering 6072.Google Scholar
  31. Pevny, T., & Fridrich, J. (2008). Detection of double-compression in jpeg images for applications in steganography. IEEE Transactions on Information Forensics and Security, 3(2), 247–258.CrossRefGoogle Scholar
  32. Popescu, A.C., & Farid, H. (2004). Statistical tools for digital forensics. In: 6th Int.l Workshop on Information Hiding, Springer, Berlin-Heidelberg, pp. 128–147.Google Scholar
  33. Popescu, A. C., & Farid, H. (2005). Exposing digital forgeries in color filter array interpolated images. IEEE Transactions on Signal Processing, 53, 3948–3959.MathSciNetCrossRefGoogle Scholar
  34. Poynton, C. A. (1996). A technical introduction to digital video. New York: John Wiley & Sons, Inc.Google Scholar
  35. Pudil, P., Ferri, F., Novovicova, J.,&Kittler, J. (1994). Floating search methods for feature selection with nonmonotonic criterion functions. In Proc. of Int. Conf on Pattern Recognition, Vol. 2, pp. 279–283.Google Scholar
  36. Santos, F., Guyomarch, P., & Bruzek, J. (2014). Statistical sex determination from craniometrics: Comparison of linear discriminant analysis, logistic regression, and support vector machines. Forensic Science International, 245, 204.e1–204.e8.CrossRefGoogle Scholar
  37. Susstrunk, S. E., Holm, J. M., & Finlayson, G. D. (2001). Chromatic adaptation performance of different rgb sensors. SPIE Proceedings Color Imaging: Device-Independent Color, Color Hardcopy, and Graphic Arts VI, San Jose, CA, 4300, 172–183.CrossRefGoogle Scholar
  38. Technical Standardization Committee on AV & IT Storage Systems and Equipment (2002) Exchangeable image file format for digital still cameras: Exif version 2.2. Tech. Rep. JEITA CP-3451.Google Scholar
  39. van de Weijer, J., Gevers, T., & Gijsenij, A. (2007). Edge-based color constancy. IEEE Transactions on Image Processing, 16, 2207–2214.MathSciNetCrossRefGoogle Scholar
  40. Zhang, W., Cao, X., Qu, Y., Hou, Y., Zhao, H., & Zhang, C. (2010). Detecting and extracting the photo composites using planar homography and graph cut. IEEE Transactions on Information Forensics and Security, 5, 544–555.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Institute for Infocomm ResearchAgency for Science, Technology and Research (A*STAR)SingaporeSingapore
  2. 2.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore

Personalised recommendations