Skip to main content
SpringerLink
Log in

Detecting Image Forgery in Single-Sensor Multispectral Images

  • Conference paper
  • First Online:
Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 816))

  • 827 Accesses

Abstract

With the advancements in digital technology, multispectral images have found use in fields like forensics, remote sensing due to their ability to perceive things which were otherwise non-existent. They are used to obtain more information about terrains, land cover and in forensics as certain things like blood stains are not visible in visible spectrum. But with newly developed photo-editing softwares, they can be easily manipulated without leaving any visible clue of manipulation, but will destroy the underlying correlation between different bands. Newly developed digital cameras employ a single sensor along with multispectral filter array (MSFA) and then interpolate the data at other locations, hence introducing a correlation between bands. In this paper, we have proposed an algorithm that can identify the lack of correlation at tampered locations in a multispectral image and can thus help in establishing the authenticity of the given multispectral image. We show the efficiency of our approach with respect to the size of tampered regions in images interpolated with one the most common demosaicking algorithm—binary tree-based edge sensing (BTES).

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 EPUB and 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

References

  1. Li, H., Luo, W., Qiu, X., Huang, J.: Image forgery localization via integrating tampering possibility maps. IEEE Trans. Inf. Forensics Secur. 12(5), 1240–1252 (2017). https://doi.org/10.1109/TIFS.2017.2656823

    Article  Google Scholar 

  2. Shen, X., Shi, Z., Chen, H.: Splicing image forgery detection using textural features based on the grey level co-occurrence matrices. IET Image Process. 11(1), 44–53 (2017). https://doi.org/10.1049/iet-ipr.2016.0238

    Article  Google Scholar 

  3. Farid, H.: How to Detect Faked Photos. Am. Sci. (2017)

    Google Scholar 

  4. Pun, C.M., Yuan, X.C., Bi, X.L.: Image forgery detection using adaptive over-segmentation and feature point matching. IEEE Trans. Inf. Forensics Secur. 10(8), 1705–1716 (2015). https://doi.org/10.1109/TIFS.2015.2423261

    Article  Google Scholar 

  5. Ferrara, P., Bianchi, T., Rosa, A.D., Piva, A.: Image forgery localization via fine-grained analysis of CFA artifacts. IEEE Trans. Inf. Forensics Secur. 7(5), 1566–1577 (2012). https://doi.org/10.1109/TIFS.2012.2202227

    Article  Google Scholar 

  6. Popescu, A.C., Farid, H.: Exposing digital forgeries in color filter array interpolated images. IEEE Trans. Signal Processing 53(10), 3948–3959 (2005)

    Article  MathSciNet  Google Scholar 

  7. Cao, H., Kot, A.C.: Accurate Detection of demosaicing regularity for digital image forensics. IEEE Trans. Inf. Forensics Secur. 4(4), 899–910 (2009)

    Article  Google Scholar 

  8. Farid, H.: A Survey of image forgery detection. IEEE Sig. Process. Mag. 26(2), 16–25 (2009)

    Article  Google Scholar 

  9. Miao, L., Qi, H., Ramanath, R., Snyder, W.E.: Binary tree-based generic demosaicking algorithm for multispectral filter arrays. IEEE Trans. Image Process. 15(11), 3550–3558 (2006)

    Article  Google Scholar 

  10. Miao, L., Qi, H.: The design and evaluation of a generic method for generating mosaicked multispectral filter arrays. IEEE Trans. Image Process. 15(9), 2780–2791 (2006)

    Article  Google Scholar 

  11. Jaiswal, S.P., Fang, L., Jakhetiya, V., Pang, J., Mueller, K., Au, O.C.: Adaptive multispectral demosaicking based on frequency-domain analysis of spectral correlation. IEEE Trans. Image Process. 26, 953–968 (2017)

    Article  MathSciNet  Google Scholar 

  12. Aggarwal, H.K., Majumdar, A., Ward, R.: A reconstruction al-gorithm for multi-spectral image demosaicing. In: Proceedings of the IASTED International Conference on Signal and Image, Banff, AL, Canada, vol. 1719 (2013)

    Google Scholar 

  13. Monno, Y., Kikuchi, S., Tanaka, M., Okutomi, M.: A practical one shot multispectral imaging system using a single image sensor. IEEE Trans. Image Process. 24(10), 3048–3059 (2015)

    Article  MathSciNet  Google Scholar 

  14. Mihoubi, S., Losson, O., Mathon, B., Macaire, L.: Multispectral demosaicing using pseudo-panchromatic image. IEEE Trans. Comput. Imaging 3(4), 982–995 (2017). https://doi.org/10.1109/TCI.2017.2691553

    Article  MathSciNet  Google Scholar 

  15. Yasuma, F., Mitsunaga, T., Iso, D., Nayar, S.K.: Generalized assorted pixel camera: post-capture control of resolution, dynamic range and spectrum. IEEE Trans. Image Process. 19(9), 2241–2253 (2010). http://www.cs.columbia.edu/CAVE/projects/gap_camera/

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Mridul Gupta

  2. Indian Institute of Technology Ropar, Rupnagar, India

    Puneet Goyal

Authors
  1. Mridul Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Puneet Goyal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mridul Gupta .

Editor information

Editors and Affiliations

  1. Department of Mathematics, South Asian University New Delhi , New Delhi, India

    Jagdish Chand Bansal

  2. Department of Mathematics, National Institute Of Technology Silchar Department of Mathematics, Silchar, Assam, India

    Kedar Nath Das

  3. Department of Mathematics and Computer Science, Faculty of Science, , Liverpool Hope University, Liverpool, UK

    Atulya Nagar

  4. Department of Mathematics, Indian Institute of Technology Roor Department of Mathematics, Roorkee, Uttarakhand, India

    Kusum Deep

  5. School of Basic Sciences, Indian Institute of Technology Bhubanesw School of Basic Sciences, Bhubaneswar, Odisha, India

    Akshay Kumar Ojha

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gupta, M., Goyal, P. (2019). Detecting Image Forgery in Single-Sensor Multispectral Images. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_67

Download citation

Publish with us

Policies and ethics

Search

Navigation