Skip to main content
Log in

Time-efficient spliced image analysis using higher-order statistics

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Image forgery is gaining huge momentum as changing the content is no longer arduous. One of the leading techniques of this category is image splicing. This technique generates a composite image formed by combining regions of images. Once the image is forged, it becomes nearly impossible for the human expert to substantiate. Hence, for detecting and localizing the spliced region in the forged image, a tool is to be developed which has become the need of the hour. Articles have been reported that one of the key ingredients for such a tool is noise inconsistency, among others. The spliced region contains the non-homogeneous distribution of noise which acts as a feature to localize it. State-of-the-art techniques based on inconsistent noise are suffering from challenges like the requirement of prior knowledge about the image, localization of spliced region and estimation of inconsistent non-gaussian noise. In this paper, a blind local noise estimation technique has been introduced using a fourth-order central moment to localize the spliced region. This paper tries to overcome the challenges of state-of-the-art techniques. Experimental analysis has been done on images of three publicly available datasets. The results are evaluated on pixel level using confusion matrix and some other performance measures. The result of the given approach is compared with previously reported techniques and found better than them.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Yang, F., Li, J., Lu, W., Weng, J.: Copy-move forgery detection based on hybrid features. Eng. Appl. Artif. Intell. 59, 73–83 (2017). https://doi.org/10.1016/j.engappai.2016.12.022

    Article  Google Scholar 

  2. Willings, A.: Famous Photoshopped and doctored images from across the ages. https://www.pocket-lint.com/apps/news/adobe/140252-30-famous-photoshopped-and-doctored-images-from-across-the-ages. Accessed 5 Mar 2019

  3. Schetinger, V., Oliveira, M.M., da Silva, R., Carvalho, T.J.: Humans are easily fooled by digital images. Comput. Gr. 68, 142–151 (2017). https://doi.org/10.1016/j.cag.2017.08.010

    Article  Google Scholar 

  4. Affaires, M.H.: Details about CCPWC (cybercrime prevention against women and children) scheme. https://mha.gov.in/division_of_mha/cyber-and-information-security-cis-division/Details-about-CCPWC-CybercrimePrevention-against-Women-and-Children-Scheme. Accessed 23 Dec 2019

  5. Nakamura, J.: Image Sensors and Signal Processing for Digital Still Camera. CRC Press, Boca Raton (2017)

    Book  Google Scholar 

  6. Meer, P., Jolion, J.M., Rosenfeld, A.: A fast parallel algorithm for blind estimation of noise variance. IEEE Trans. Pattern Anal. Mach. Intell. 12, 216–223 (1990). https://doi.org/10.1109/34.44408

    Article  Google Scholar 

  7. Liu, C., Szeliski, R., Kang, S.B., Zitnick, C.L., Freeman, W.T.: Automatic estimation and removal of noise from a single image. IEEE Trans. Pattern Anal. Mach. Intell. 30, 299–314 (2008). https://doi.org/10.1109/TPAMI.2007.1176

    Article  Google Scholar 

  8. Aja-Fernández, S., Vegas-Sánchez-Ferrero, G., Martín-Fernández, M., Alberola-López, C.: Automatic noise estimation in images using local statistics. Additive and multiplicative cases. Image Vis. Comput. 27, 756–770 (2009). https://doi.org/10.1016/j.imavis.2008.08.002

    Article  Google Scholar 

  9. Liu, X., Tanaka, M., Okutomi, M.: Single-image noise level estimation for blind denoising. IEEE Trans. Image Process. 22, 5226–5237 (2013). https://doi.org/10.1109/TIP.2013.2283400

    Article  Google Scholar 

  10. Pan, X., Zhang, X., Lyu, S.: Blind local noise estimation for medical images reconstructed from rapid acquisition. Med. Imaging Process. 8314, 83143R (2012). https://doi.org/10.1117/12.910857

    Article  Google Scholar 

  11. Mihcak, M.K., Kozintsev, I., Ramchandran, K.: Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising. In: 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings. ICASSP99 (Cat. No.99CH36258), vol. 6, pp. 7–10 (1999). https://doi.org/10.1109/ICASSP.1999.757535

  12. Coifman, R.R., Donoho, D.L.: Translation-invariant de-noising. Wavel. Stat. 103, 125–150 (1995). https://doi.org/10.1007/978-1-4612-2544-7_9

    Article  MATH  Google Scholar 

  13. Lyu, S., Pan, X., Zhang, X.: Exposing region splicing forgeries with blind local noise estimation. Int. J. Comput. Vis. 110, 202–221 (2013). https://doi.org/10.1007/s11263-013-0688-y

    Article  Google Scholar 

  14. Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41, 613–626 (1995)

    Article  MathSciNet  Google Scholar 

  15. Immerkær, J.: Fast noise variance estimation. Comput. Vis. Image Underst. 64, 300–302 (1996). https://doi.org/10.1006/cviu.1996.0060

    Article  Google Scholar 

  16. Muhammad, N., Bibi, N., Jahangir, A., Mahmood, Z.: Image denoising with norm weighted fusion estimators. Pattern Anal. Appl. 21, 1013–1022 (2018). https://doi.org/10.1007/s10044-017-0617-8

    Article  MathSciNet  Google Scholar 

  17. Agarwal, S., Chand, S.: Image forgery detection using multi scale entropy filter and local phase quantization. Int. J. Image Gr. Signal Process. 8, 64–74 (2015). https://doi.org/10.5815/ijigsp.2015.10.08

    Article  Google Scholar 

  18. Muhammad, G., Al-Hammadi, M.H., Hussain, M., Bebis, G.: Image forgery detection using steerable pyramid transform and local binary pattern. Mach. Vis. Appl. 25, 985–995 (2014). https://doi.org/10.1007/s00138-013-0547-4

    Article  Google Scholar 

  19. Wang, W., Dong, J., Tan, T.: Effective image splicing detection based on image chroma. In: IEEE International Conference on Image Processing, pp. 1257–1260 (2009)

  20. He, Z., Lu, W., Sun, W., Huang, J.: Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recognit. 45, 4292–4299 (2012). https://doi.org/10.1016/j.patcog.2012.05.014

    Article  Google Scholar 

  21. Ng, T.T., Chang, S.F.: Blind detection of photomontage using higher order statistics. ADVENT Technical Report, Columbia University, pp. V-688–V-691 (2004). https://doi.org/10.1109/ISCAS.2004.1329901

  22. Amerini, I., Becarelli, R., Caldelli, R., Del Mastio, A.: Splicing forgeries localization through the use of first digit features. In: IEEE International Workshops on Information Forensics Security. WIFS 2014, pp. 143–148 (2015). https://doi.org/10.1109/WIFS.2014.7084318

  23. Li, W., Yuan, Y., Yu, N.: Passive detection of doctored JPEG image via block artifact grid extraction. Sig. Process. 89, 1821–1829 (2009). https://doi.org/10.1016/j.sigpro.2009.03.025

    Article  MATH  Google Scholar 

  24. Bianchi, T., De Rosa, A., Piva, A.: Improved DCT coefficient analysis for forgery localization in JPEG images. In; ICASSP, IEEE International Conference on Acoustics on Speech Signal Processing Proceedings, pp. 2444–2447 (2011). https://doi.org/10.1109/ICASSP.2011.5946978

  25. Fan, J., Cao, H., Kot, A.C.: Estimating EXIF parameters based on noise features for image manipulation detection. IEEE Trans. Inf. Forensics Secur. 8, 608–618 (2013). https://doi.org/10.1109/TIFS.2013.2249064

    Article  Google Scholar 

  26. Kee, E., Johnson, M.K., Farid, H.: Digital image authentication from JPEG headers. IEEE Trans. Inf. Forensics Secur. 6, 1066–1075 (2011). https://doi.org/10.1109/TIFS.2011.2128309

    Article  Google Scholar 

  27. De Carvalho, T.J., Riess, C., Angelopoulou, E., Pedrini, H., Rocha, A.D.R.: Exposing digital image forgeries by illumination color classification. IEEE Trans. Inf. Forensics Secur. 8, 1182–1194 (2013). https://doi.org/10.1109/TIFS.2013.2265677

    Article  Google Scholar 

  28. Riess, C., Unberath, M., Naderi, F., Pfaller, S., Stamminger, M., Angelopoulou, E.: Handling multiple materials for exposure of digital forgeries using 2-D lighting environments. Multimed. Tools Appl. 76, 4747–4764 (2017). https://doi.org/10.1007/s11042-016-3655-0

    Article  Google Scholar 

  29. Peng, B., Wang, W., Dong, J., Tan, T.: Optimized 3D lighting environment estimation for image forgery detection. IEEE Trans. Inf. Forensics Secur. 12, 479–494 (2017). https://doi.org/10.1109/TIFS.2016.2623589

    Article  Google Scholar 

  30. Zhang, W., Cao, X., Qu, Y., Hou, Y., Zhao, H., Zhang, C.: Detecting and extracting the photo composites using planar homography and graph cut. IEEE Trans. Inf. Forensics Secur. 5, 544–555 (2010). https://doi.org/10.1109/TIFS.2010.2051666

    Article  Google Scholar 

  31. Zhu, N., Li, Z.: Blind image splicing detection via noise level function. Signal Process. Image Commun. 68, 181–192 (2018). https://doi.org/10.1016/j.image.2018.07.012

    Article  Google Scholar 

  32. Yao, H., Wang, S., Zhang, X., Qin, C., Wang, J.: Detecting image splicing based on noise level inconsistency. Multimed. Tools Appl. 76, 12457–12479 (2017). https://doi.org/10.1007/s11042-016-3660-3

    Article  Google Scholar 

  33. Mahdian, B., Saic, S.: Using noise inconsistencies for blind image forensics. Image Vis. Comput. 27, 1497–1503 (2009). https://doi.org/10.1016/j.imavis.2009.02.001

    Article  Google Scholar 

  34. Popescu, A.C., Farid, H.: Statistical Tools for Digital Forensics. Springer, Berlin (2004)

    Book  Google Scholar 

  35. McLaughlin, S., Stogioglou, A., Fackrell, J.: Introducing higher order statistics (HOS) for the detection of nonlinearities (1995). http://www1.maths.leeds.ac.uk/applied/news.dir/issue2/hos_intro.html

  36. Jöreskog, K.G.: Formulas for skewness and kurtosis. Sci. Softw. Int. http//www.ssicentral.com/lisrel. (1999)

  37. Jiang, W., Shen, T.Z., Jiang, W., Lam, K.M.: Efficient edge detection using simplified gabor wavelets. IEEE Trans. Syst. Man. Cybern. 39, 1036–1047 (2009). https://doi.org/10.1109/TSMCB.2008.2011646

    Article  Google Scholar 

  38. Hsu, Y.F., Chang, S.F.: Detecting image splicing using geometry invariants and camera characteristics consistency. In: International Conference on Multimedia and Expo, Toronto, Canada (2006)

  39. Dong, J., Wang, W.: CASIA v1.0 and CASIA v2.0 Image Splicing Dataset. Natl. Lab. Pattern Recognition, Inst. Autom. Chinese Acad. Sci. Corel Image Database

  40. IFS, T.: IEEE IFS-TC image forensics challenge database. IEEE Signal Process

  41. Göngör, F., Tutsoy, Ö.: Design and implementation of a facial character analysis algorithm for humanoid robots. Robotica 37, 1850–1866 (2019). https://doi.org/10.1017/S0263574719000304

    Article  Google Scholar 

  42. Chenggang, Y., Gong, B., Wei, Y., Gao, Y.: Deep multi-view enhancement hashing for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. (2020). https://doi.org/10.1109/TPAMI.2020.2975798

    Article  Google Scholar 

  43. Yan, C., Shao, B., Zhao, H., Ning, R., Zhang, Y., Xu, F.: 3D Room layout estimation from a single RGB image. IEEE Trans. Multimed. (2020). https://doi.org/10.1109/TMM.2020.2967645

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ankit Kumar Jaiswal.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jaiswal, A.K., Srivastava, R. Time-efficient spliced image analysis using higher-order statistics. Machine Vision and Applications 31, 56 (2020). https://doi.org/10.1007/s00138-020-01107-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-020-01107-z

Keywords

Navigation