Skip to main content
SpringerLink
Log in

Expose noise level inconsistency incorporating the inhomogeneity scoring strategy

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Estimating variances in noise is of key importance in many image processing applications, such as filtering, enhancement, quality assessment, and detecting forgery. For the existing detection methods that are based on inconsistencies in noise, the conventional approach is to estimate the noise variance of each region first and then identify the regions with extremely higher or lower variance as splicing regions. However, due to the impossibility of completely separating image noise and inherent texture, inevitably, each estimate is overestimated, especially for regions that have more complex textures. In this paper, we consider the issue that the estimation of the noise of each region frequently is inaccurate due to the complexity of the texture of the region. Based on this consideration and motivated by the scoring strategy-based, object-proposal technique, an approach that incorporates the inhomogeneity scoring strategy is proposed to provide a more convincing result to expose image-splicing manipulations. Specifically, first, the image is segmented into small patches, and the noise variance of each patch is computed by using the kurtosis concentration-based pixel-level noise estimation method. Then, the inhomogeneity score is computed using the spectral residual-based saliency measurement method. After using a linear equation fitting based on the estimated sample of variance and the inhomogeneity score of each patch, the suspicious region can be identified by seeking the conjunct patches that are out of the linear constraint. The experimental results demonstrated the efficacy and robustness of the proposed method.

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

Access this article

Log in via an institution

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

Notes

  1. Both authentic and spliced images can be downloaded from the website of http://www.ee.columbia.edu/ln/dvmm/downloads/authsplcuncmp/ .

  2. CASIA 2.0 Tampered Image Detection Evaluation Database can be downloaded from the website of http://forensics.idealtest.org .

References

  1. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Suesstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2281

    Article  Google Scholar 

  2. Alexe B, Deselaers T, Ferrari V (2012) Measuring the objectness of image windows. IEEE Trans Pattern Anal Mach Intell 34(11):2189–2202

    Article  Google Scholar 

  3. Bahrami K, Kot AC, Li L, Li H (2015) Blurred image splicing localization by exposing blur type inconsistency. IEEE Trans. Inf. Forensics Secur 10(5):999–1009

    Article  Google Scholar 

  4. Bianchi T, Piva A (2012) Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Trans. Inf. Forensics Secur 7(3):1003–1017

    Article  Google Scholar 

  5. Chen B, Coatrieux G, Wu J, Dong Z, Coatrieux LJ, Shu H (2015) Fast computation of sliding discrete Tchebichef moments and its application in duplicated regions detection. IEEE Trans Signal Process 63(20):5424–5436

    Article  MathSciNet  Google Scholar 

  6. Chierchia G, Poggi G, Sansone C, Verdoliva L (2014) A Bayesian-MRF approach for PRNU-based image forgery detection. IEEE Trans. Inf. Forensics Secur 9(4):554–567

    Article  Google Scholar 

  7. Ferreira A, Felipussi SC, Alfaro C, Fonseca P, Vargas-Muñoz JE, Dos Santos JA, Rocha A (2016) Behavior knowledge space-based fusion for copy-move forgery detection. IEEE Trans Image Process 25(10):4729–4742

    Article  MathSciNet  Google Scholar 

  8. Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition pp 1–8

  9. Korus P, Huang J (2017) Multi-scale analysis strategies in PRNU-based tampering localization. IEEE Trans. Inf. Forensics Secur 12(4):809–824

    Article  Google Scholar 

  10. Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans. Inf. Forensics Secur 10(3):507–518

    Article  Google Scholar 

  11. Li B, Ng TT, Li X, Tan S, Huang J (2015) Revealing the trace of high-quality JPEG compression through quantization noise analysis. IEEE Trans. Inf. Forensics Secur 10(3):558–573

    Article  Google Scholar 

  12. Liu C, Szeliski R, Kang SB, Zitnick CL, Freeman WT (2008) Automatic estimation and removal of noise from a single image. IEEE Trans Pattern Anal Mach Intell 30(2):299–314

    Article  Google Scholar 

  13. Lyu S, Pan X, Zhang X (2014) Exposing region splicing forgeries with blind local noise estimation. Int J Comput Vis 110:202–221

    Article  Google Scholar 

  14. Mahdian B, Saic S (2009) Using noise inconsistencies for blind image forensics. Image Vis Comput 27(10):1497–1503

    Article  Google Scholar 

  15. Pan X, Zhang X, Lyu S (2011) Exposing image forgery with blind noise estimation. In the 13th ACM Workshop on Multimedia and Security, pp. 15–20

  16. Pasquale F, Tiziano B, De Rosa A, Piva A (2012) Image forgery localization via fine-grained analysis of CFA artifacts. IEEE Trans. Inf. Forensics Secur 7(5):1566–1577

    Article  Google Scholar 

  17. Pyatykh S, Hesser J, Zheng L (2013) Image noise level estimation by principal component analysis. IEEE Trans Image Process 22(2):687–699

    Article  MathSciNet  MATH  Google Scholar 

  18. Qin C, Chen X, Ye D, Wang J, Sun X (2016) A novel image hashing scheme with perceptual robustness using block truncation coding. Inf Sci 361-362:84–99

    Article  Google Scholar 

  19. Qin C, Ji P, Zhang X, Dong J, Wang J (2017) Fragile image watermarking with pixel-wise recovery based on overlapping embedding strategy. Signal Process 138:280–293

    Article  Google Scholar 

  20. Qureshi MA, Deriche M (2015) A bibliography of pixel-based blind image forgery detection techniques. Signal Processing-Image Communication 39:46–74

    Article  Google Scholar 

  21. Rakhshanfar M, Amer AM (2016) Estimation of gaussian, poissonian gaussian, and processed visual noise and its level function. IEEE Trans Image Process 25(9):4172–4185

    MathSciNet  Google Scholar 

  22. Rota P, Sangineto E, Conotter V, Pramerdorfer C (2016) Bad teacher or unruly student: can deep learning say something in image forensics analysis? In Proc. of the 23rd International Conference on Pattern Recognition (ICPR), pp. 2503–2508

  23. Stamm MC, Wu M, Liu KJR (2013) Information forensics: an overview of the first decade. IEEE Access 1:167–200

    Article  Google Scholar 

  24. Valsesia D, Coluccia G, Bianchi T, Magli E (2015) Compressed fingerprint matching and camera identification via random projections. IEEE Trans. Inf. Forensics Secur 10(7):1472–1485

    Article  Google Scholar 

  25. Wang J, Li T, Shi Y, Lian S, Ye J (2016) Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimedia Tools and Applications (in press, DOI: 10.1007/s11042-016-4153-0)

  26. Wei W, Wang S, Zhang X, Tang Z (2010) Estimation of image rotation angle using interpolation-related spectral signatures with application to blind detection of image forgery. IEEE Trans. Inf. Forensics Secur 5(3):507–517

    Article  Google Scholar 

  27. Wu CH, Chang HH (2015) Superpixel-based image noise variance estimation with local statistical assessment. EURASIP Journal on Image and Video Processing 2015: 38

  28. Yao H, Wang S, Zhang X, Qin C, Wang J (2017) Detecting image splicing based on noise level inconsistency. Multimedia Tools and Applications 76(10):12457–12479

    Article  Google Scholar 

  29. Zeng H, Zhan Y, Kang X, Lin X (2017) Image splicing localization using PCA-based noise level estimation. Multimedia Tools and Applications 76(4):4783–4799

    Article  Google Scholar 

  30. Zoran D, Weiss Y (2009) Scale invariance and noise in natural images. In Proc. of IEEE International Conference on Computer Vision, pp. 2209–2216

Download references

Acknowledgements

We would like to thank Drs. Siwei Lyu and Hui Zeng for kindly sharing the codecs of their work. This work was supported in part by the National Natural Science Foundation of China (61702332, 61672354, 61562007), Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (MIMS16-03), the PAPD Fund, and the CICAEET Fund.

Author information

Authors and Affiliations

  1. Shanghai Key Lab of Modern Optical System, and Engineering Research Center of Optical Instrument and System, Ministry of Education, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Heng Yao

  2. Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004, China

    Heng Yao & Zhenjun Tang

  3. College of Information Engineering, Shanghai Maritime University, Shanghai, 200135, China

    Fang Cao

  4. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jinwei Wang

  5. School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China

    Tong Qiao

Authors
  1. Heng Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fang Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenjun Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinwei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tong Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Heng Yao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, H., Cao, F., Tang, Z. et al. Expose noise level inconsistency incorporating the inhomogeneity scoring strategy. Multimed Tools Appl 77, 18139–18161 (2018). https://doi.org/10.1007/s11042-017-5206-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5206-8

Keywords

Search

Navigation