Fraudulent Image Recognition Using Stable Inherent Feature

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 425)

Abstract

To prove authenticity and originality of images, many techniques were recently released. This paper proposes a pixel based forgery detection technique for identifying forged images, which is an effective method for finding tampering in images. The proposed method detects splicing and copy-move forgery in images by locating the forged components in the input image. Splicing is the process of copying a component from an image and pasted to another image. Copy-move forgery is the process of copying a component from an image and pasted to another portion of the same image. To find the forged component in the input image, the noise variance remaining after denoising method and SURF features are used. In order to locate spliced component, image segmentation is done before finding the number of components in the image. For segmentation, segmentation based on combining spectral and texture features are used. To identify the number of components in the image, fuzzy c-means clustering is used. In order to locate copy-move forgery, SURF features are detected first and extracted for finding the similarity between keypoints. The experiment results show that the proposed method is very good at identifying whether an image is forged or not. The proposed method gives a high speed performance compared to the state-of-the-art methods. Results gained through the experiments on both manually edited images and visually realistic real images shows the effectiveness of the proposed method.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lee, J.-C., Chang, C.-P., Chen, W.-K.: Detection of copy–move image forgery using histogram of orientated gradients. Inf. Sciences (2015)Google Scholar
  2. 2.
    Amerini, I., Ballan, L., Caldelli, R., et al.: Copy-move forgery detection and localization by means of robust clustering with J-linkage. Signal Processing: Image Communication 28, 659–669 (2013)Google Scholar
  3. 3.
    Chang, I.-C., Yu, J.C., Chang, C.-C.: A forgery detection algorithm for exemplar-based inpainting images using multi-region relation. Image and Vision Computing 31, 57–71 (2013)CrossRefGoogle Scholar
  4. 4.
    Bravo-Solorio, S., Nandi, A.K.: Automated detection and localisation of duplicated regions affected by reflection, rotation and scaling in image forensics. Signal Process. 91, 1759–1770 (2011)CrossRefMATHGoogle Scholar
  5. 5.
    Pan, X., Lyu, S.: Region duplication detection using image feature matching. IEEE Trans. on Inf. Forensics and Secur. 5(4), 857–867 (2010)CrossRefGoogle Scholar
  6. 6.
    Zhang, W., Cao, X., Qu, Y., et al.: Detecting and extracting the photo composites using planar homography and graph cut. IEEE Trans. Inf. Forensics Secur. 5(3), 544–555 (2010)CrossRefGoogle Scholar
  7. 7.
    He, Z., Lu, W., et al.: Digital image splicing detection based on markov features in dct and dwt domain. Pattern Recognit. 45(2012), 4292–4299 (2012)CrossRefGoogle Scholar
  8. 8.
    Birajdar, G.K., Mankar, V.H.: Digital image forgery detection using passive tech-niques: a survey. Digit. Investig. 10, 226–245 (2013)CrossRefGoogle Scholar
  9. 9.
    Liu, K.C.: Color image watermarking for tamper proofing and pattern-based recovery. IET Image Process. 6(5), 445–454 (2012)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Han, Q., Han, L., et al.: Dual watermarking for image tamper detection and self-recovery. In: Proc. of the 9th Int. Conference on Intell. Inf. Hiding and Multimedia Signal Process., pp. 33–36 (2013)Google Scholar
  11. 11.
    Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. on Inf. Forensics and Secur. 1(2), 205–214 (2006)CrossRefGoogle Scholar
  12. 12.
    Li, C.-T.: Source camera identification using enhanced sensor pattern noise. IEEE Trans. on Inf. Forensics and Secur. 5(2), 280–287 (2010)CrossRefGoogle Scholar
  13. 13.
    Kang, X., Li, Y., Qu, Z., Huang, J.: Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Trans. on Inf. Forensics and Secur. 7(2), 393–402 (2012)CrossRefGoogle Scholar
  14. 14.
    Tomioka, Y., Ito, Y., Kitazawa, H.: Robust digital camera identification based on pairwise magnitude relations of clustered sensor pattern noise. Trans. on Inf. Forensics and Secur. 8(12), 1986–1995 (2013)CrossRefGoogle Scholar
  15. 15.
    Wu-Chih, H., Dai, J.-S., Jian, J.-S.: Effective composite image detection method based on feature inconsistency of image components. Digit. Signal Processing 39, 50–62 (2015)CrossRefGoogle Scholar
  16. 16.
    Mahdian, B., Saic, S.: Using noise inconsistencies for blind image forensics. Image Vis. Compu., Special Section: Computer Vision Methods for Ambient Intell. 10, 1497–1503 (2009)CrossRefGoogle Scholar
  17. 17.
    Comaniciu, D., Meer, P.: Mean shift:a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar
  18. 18.
    Levin, A., Rav-Acha, A., Lischinski, D.: Spectral matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1699–1712 (2008)CrossRefGoogle Scholar
  19. 19.
    Hardie, R.C., Boncelet, C.G.: Lum filters:a class of rank-order-based filters for smoothing and sharpening. IEEE Trans. Signal Process. 41(3), 1061–1076 (1993)CrossRefMATHGoogle Scholar
  20. 20.
    Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. on Pattern Analysis and Machine Intell. 11(7), 674–693 (1989)CrossRefMATHGoogle Scholar
  21. 21.
    Yuan, J., Wang, D., Li, R.: Remote sensing image segmentation by combining spectral and texture Features. IEEE Trans. on Geoscience and Remote Sensing 52(1), 16–24 (2014)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Fan, J., Cao, H., Kot, A.C.: Estimating EXIF parameters based on noise features for image manipulation detection. IEEE Trans. Inf. Forensics Secur. 8(4), 608–618 (2013)CrossRefGoogle Scholar
  23. 23.
    Bianchi, T., Piva, A.: Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Trans. Inf. Forensics Secur. 7(3), 1003–1017 (2012)CrossRefGoogle Scholar
  24. 24.
    Lin, Z., He, J., Tang, X., Tang, C.K.: Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recognit. 42(11), 2492–2501 (2009)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Deny Williams
    • 1
  • G. Krishnalal
    • 1
  • V. P. Jagathy Raj
    • 2
  1. 1.Amal Jyothi College of EngineeringKottayamIndia
  2. 2.School of Management StudiesCUSATKochiIndia

Personalised recommendations