Skip to main content
SpringerLink
Log in

Fraudulent Image Recognition Using Stable Inherent Feature

  • Conference paper
  • First Online:
Book cover Advances in Signal Processing and Intelligent Recognition Systems

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

  • 1681 Accesses

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  5. Pan, X., Lyu, S.: Region duplication detection using image feature matching. IEEE Trans. on Inf. Forensics and Secur. 5(4), 857–867 (2010)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  8. Birajdar, G.K., Mankar, V.H.: Digital image forgery detection using passive tech-niques: a survey. Digit. Investig. 10, 226–245 (2013)

    Article  Google Scholar 

  9. Liu, K.C.: Color image watermarking for tamper proofing and pattern-based recovery. IET Image Process. 6(5), 445–454 (2012)

    Article  MathSciNet  Google Scholar 

  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. 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)

    Article  Google Scholar 

  12. Li, C.-T.: Source camera identification using enhanced sensor pattern noise. IEEE Trans. on Inf. Forensics and Secur. 5(2), 280–287 (2010)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  18. Levin, A., Rav-Acha, A., Lischinski, D.: Spectral matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1699–1712 (2008)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Amal Jyothi College of Engineering, Kottayam, India

    Deny Williams & G. Krishnalal

  2. School of Management Studies, CUSAT, Kochi, India

    V. P. Jagathy Raj

Authors
  1. Deny Williams
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Krishnalal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. P. Jagathy Raj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deny Williams .

Editor information

Editors and Affiliations

  1. and Management – Kerala (IIITM-K), Indian Inst. of Information Technology, Trivandrum, Kerala, India

    Sabu M. Thampi

  2. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Sanghamitra Bandyopadhyay

  3. Department of Electrical, Ryerson University, Toronto, Ontario, Canada

    Sri Krishnan

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. Computer Engineering Department, Vladimir State University, Vladimir Region, Russia

    Sergey Mosin

  6. School of Electrical, Nanyang Technological University, Singapore, Singapore

    Maode Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Williams, D., Krishnalal, G., Jagathy Raj, V.P. (2016). Fraudulent Image Recognition Using Stable Inherent Feature. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28658-7_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28656-3

  • Online ISBN: 978-3-319-28658-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation