Skip to main content

A Fuzzy-Based Multi-Criteria Decision-Making Approach for the Selection of Digital Image Forensic Tools

  • 219 Accesses

Part of the Studies in Systems, Decision and Control book series (SSDC,volume 407)

Abstract

Digital image forensic science is a sub-research area of multimedia security whose objective is to check the authenticity of digital images. Different algorithms as well as tools have been developed to check the forged images. In literature, less attention is given on the evaluation and selection of the digital image forensic tools based on different features like error level analysis, metadata analysis, double joint photographic expert group, etc. Therefore, to address this issue, in this chapter an algorithm has been developed for the selection of the digital image forensic tools based on the ranking values. The ranking values of the digital image forensic tools are computed using TOPSIS method by using the triangular fuzzy numbers. The utilization of the proposed method is discussed with the help of an example in which following tools have been considered during the analysis, i.e., FotoForensics, JPEGsnoop, Forensically, Ghiro, and Izitru.

Keywords

  • Digital image forensic
  • Multi-criteria decision-making
  • Fuzzy logic
  • Technique for order of preference by similarity to ideal solutions
  • TOPSIS

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-16-7414-3_5
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   129.00
Price excludes VAT (USA)
  • ISBN: 978-981-16-7414-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   169.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3

Abbreviations

AHP:

Analytic Hierarchy Process

AIFD:

Active Image Forgery Detection

C:

Criteria

CC:

Closeness Coefficients

DIF:

Digital Image Forensic

DM:

Decision-Makers

ELA:

Error Level Analysis

FDM:

Fuzzy Decision Matrix

FNIS:

Fuzzy Negative Ideal Solution

FPIS:

Fuzzy Positive Ideal Solution

FST:

Fuzzy Set Theory

H:

High

JPEG:

Joint Photographic Expert Group

L:

Low

M:

Medium

MA:

Metadata

MCDM:

Multi-Criteria Decision-Making

PIFD:

Passive Image Forgery Detection

S:

Strong

T:

Tools

TFNs:

Triangular Fuzzy Numbers

TOPSIS:

Technique for Order of Preference by Similarity to Ideal Solution

VH:

Very High

VL:

Very Low

VS:

Very Strong

VW:

Very Weak

W:

Weak

WN:

Weighted Normalized Fuzzy Decision Matrix

\(\sum \) :

Summation

min ():

Returns minimum value

max ():

Returns maximum value

References

  1. Parveen, A., Khan, Z.H., Ahmad, S.N.: Block-based copy-move image forgery detection using DCT. Iran J. Comput. Sci. 2, 89–99 (2019)

    CrossRef  Google Scholar 

  2. Qureshi, M.A., Deriche, M.: A bibliography of pixel-based blind image forgery detection techniques. Signal Process. Image Commun. 39(A), 46–74 (2015)

    Google Scholar 

  3. Photo Tampering Throughout History. http://pth.izitru.com/1860_13_00.html

  4. Parveen, A., Tayal, A.: An algorithm to detect the forged part in an image. In: IEEE International Conference on Communication and Signal Processing, pp. 1486–1490 (2016)

    Google Scholar 

  5. Parveen, A., Khan, Z.H., Ahmad, S.N.: Classification and evaluation of digital forensic tools. TELKOMNIKA Telecommun. Comput. Electron. Control 18(6), 3096–3106 (2020)

    Google Scholar 

  6. Mardani, A., Jusoh, A., Nor, K. MD., Khalifah, Z., Zakwan, N., Valipour, A.: multiple criteria decision making techniques and their applications-a review of the literature from 2000 to 2014. Econ. Res. 28(1), 516–571 (2015)

    Google Scholar 

  7. Sadiq, M., Khan, S., Mohammad, C.W.: Selection of software requirements using TOPSIS under fuzzy environment. Int. J. Comput. Appl. 1–10 (2020)

    Google Scholar 

  8. Chen, C.T.: Extension of the TOPSIS for group decision making under fuzzy environment. Fuzzy Sets Syst. 114, 1–9 (2000)

    CrossRef  Google Scholar 

  9. Ertugrul, I., Karakasoglu, N.: Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection. Int. J. Adv. Manuf. Technol. 39, 783–795 (2008)

    Google Scholar 

  10. Behzadian, M., Otaghsara, S.K., Yazdani, M., Ignatius, J.: A State-of the-art survey of TOPSIS applications. Expert Syst. Appl. 39, 13051–13069 (2012)

    CrossRef  Google Scholar 

  11. Kumar, M., Srivastava, S.: Image forgery detection based on physics and pixels. Aust. J. Forensic Sci. 51(2), 119–134 (2019)

    MathSciNet  CrossRef  Google Scholar 

  12. Mahdian, B., Saic, S.: A bibliography on blind methods for identifying image forgery. Signal Proces. Image Commun. 25(6), 389–399 (2010)

    CrossRef  Google Scholar 

  13. Parveen, A., Khan, Z.H., Ahmad, S.N.: Identification of the forged images using image forensic tools. In: Proceedings of 2nd International Conference on Communication and Computing Systems, CRC-Press, Taylor and Francis, pp. 1–6 (2018)

    Google Scholar 

  14. Parveen, A., Khan, Z.H., Ahmad, S.N.: Pixel based copy-move image forgery detection techniques: a systematic literature review. In: Proceedings of the 5th IEEE International Conference on Computing for Sustainable Global Development, organized by BVICAM, New Delhi, India, pp. 663–668, (2018).

    Google Scholar 

  15. Kitchenham, B.: Procedure for performing Systematic Review, Joint Technical Report, Software Engineering Group, Department of Computer Science. Keele University, United Kingdom and Empirical Software Engineering, national ICT Australia (2004)

    Google Scholar 

  16. Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4(2), 103–111 (1996)

    CrossRef  Google Scholar 

  17. Sadiq, M., Jain, S.K.: Applying fuzzy preference relation for requirements prioritization in goal oriented requirements elicitation process. Int. J. Syst. Assur. Eng. Manag. 5(4), 711–723 (2014)

    CrossRef  Google Scholar 

  18. Shih, H.-S., Shyur, H.-J., Lee, E.S.: An extension of TOPSIS for group decision making. Math. Comput. Modell. 45, 801–813 (2007)

    Google Scholar 

  19. Sadiq, M., Jain, S.K.: An insight into requirements engineering processes. In: 3rd International Conference on Advances in Communication, Network, and Computing LNCSIT, Chennai, pp. 313–318 (2012)

    Google Scholar 

  20. Chen, C.T.: Extensions of the TOPSIS for group decisions making under fuzzy environment. Fuzzy Sets Syst. 114, 1–9 (2000)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Parveen, A., Khan, Z.H., Ahmad, S.N. (2022). A Fuzzy-Based Multi-Criteria Decision-Making Approach for the Selection of Digital Image Forensic Tools. In: Kulkarni, A.J. (eds) Multiple Criteria Decision Making. Studies in Systems, Decision and Control, vol 407. Springer, Singapore. https://doi.org/10.1007/978-981-16-7414-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-7414-3_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7413-6

  • Online ISBN: 978-981-16-7414-3

  • eBook Packages: EngineeringEngineering (R0)