Skip to main content
SpringerLink
Log in

Gabor Filter and Centre Symmetric-Local Binary Pattern based technique for forgery detection in images

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

Abstract

This paper presents a new approach for the detection of copy-move image forgery, a commonly employed technique for image manipulation. The proposed method combines a modified version of the Gabor Filter and Centre Symmetric Local Binary Pattern (CS-LBP) for feature extraction, aiming to meet the growing demand for accurate forgery detection. The process involves pre-processing the image and extracting features using the Gabor filter and CS-LBP at varying scales and orientations. Key points are matched using the Manhattan distance to identify forged regions. Classification of the forged images is achieved using Hybrid Neural Networks with Decision Tree (HNN-DT). In order to assess the performance of the presented method, diverse image datasets are used and compared to existing feature extraction techniques. The results demonstrate the effectiveness of the modified Gabor filter with CS-LBP in accurately classifying forged images. Specifically, the HNN-DT method with Gabor filter CS-LBP feature extraction surpasses HNN-DT with SURF and PCA feature extraction in terms of classification accuracy and overall performance. Evaluation on the CoMoFoD database confirms superior results compared to existing techniques, establishing the proposed method as a reliable approach for distinguishing between authentic and forged images. Consequently, it serves as a robust solution for image classification and forgery detection. The presented work focuses on detecting mainly three types of image forgery, namely retouching, splicing, and cloning. It is applicable to various image formats, including JPEG and BMP, and is designed specifically for forensic applications in image forgery detection.

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
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data availability

Data will be made available on reasonable request.

Code availability

Not applicable.

References

  1. Ganesan C, Bhuma VR (2014) Digital image forgery detection using color illumination and decision tree classification Int J Eng Res Appl 2014:18–23

  2. Mohammadian Fini R, Mahlouji M, Shahidinejad A (2022) Performance improvement in face recognition system using optimized Gabor filters. Multimed Tools Appl 81:38375–38408

  3. Begum M, Uddin MS (2020) Digital image watermarking techniques: a review. Information 11(2):110

    Article  Google Scholar 

  4. Jha K, Shirwadkar K, Narvekar T, Kothari I, Jacob S (2020) Digital image forgery detection. Int Res J Eng Technol 7(5):4542–4547

  5. Jingade RR, Kunte RS (2022) DOG-ADTCP: a new feature descriptor for protection of face identification system. Expert Syst Appl 201:117207

    Article  Google Scholar 

  6. Li C, Huang W, Huang Y (2022) Gabor log-euclidean gaussian and its fusion with deep network based on self-attention for face recognition. Appl Soft Comput 116:108210

    Article  Google Scholar 

  7. Kashyap A, Suresh B, Gupta H (2022) Robust detection of copy-move forgery based on wavelet decomposition and firefly algorithm. Comput J 65(4):983–996

    Article  MathSciNet  Google Scholar 

  8. Diwan A, Sharma R, Roy AK, Mitra SK (2021) Keypoint based comprehensive copy-move forgery detection. IET Image Proc 15(6):1298–1309

    Article  Google Scholar 

  9. Kumar S, Desai J, Mukherjee S (2013) A fast DCT based method for copy move forgery detection. In Image Information Processing (ICIIP), 2013 IEEE Second International Conference on (pp. 649–654). IEEE

  10. Barni M, Phan QT, Tondi B (2020) Copy moves source-target disambiguation through multi-branch CNNs. IEEE Trans Inf Forensics Secur 16:1825–1840

    Article  Google Scholar 

  11. Gardella M, Musé P, Morel JM, Colom M (2021) Forgery detection in digital images by multi-scale noise estimation. J Imaging 7(7):119

    Article  Google Scholar 

  12. Amiri E, Mosallanejad A, Sheikhahmadi A (2021) Copy-move forgery detection by an optimal keypoint on SIFT (OKSIFT) Method. J Comput Rob 14(2):11–19

    Google Scholar 

  13. Niyishaka P, Bhagvati C (2021) Image splicing detection technique based on illumination-reflectance model and LBP. Multimed Tools Appl 80(2):2161–2175

    Article  Google Scholar 

  14. Al Zahir S, Hammad R (2020) Image forgery detection using image similarity. Multimed Tools Appl 79(39):28643–28659

    Article  Google Scholar 

  15. Javed AR, Jalil Z, Zehra W, Gadekallu TR, Suh DY, Piran MJ (2021) A comprehensive survey on digital video forensics: taxonomy, challenges, and future directions. Eng Appl Artif Intell 106:104456

    Article  Google Scholar 

  16. VinodKumar RS (2016) A comparative analysis of histogram of gradient (HOG), Gabor filter bank and DCT based feature extraction methods used for fingerprint recognition Int J Sci Eng Res 7(4):321–326

  17. Yang J, Xiao S, Li A, Lan G, Wang H (2021) Detecting fake images by identifying potential texture difference. Futur Gener Comput Syst 125:127–135

    Article  Google Scholar 

  18. Abinaya D, Priyanka C, Stefinjain MR, Venkatesan GDP, Kamalraj S (2021) Classification of Facial Expression Recognition using Machine Learning Algorithms. In Journal of Physics: Conference Series (Vol. 1937, No. 1, p. 012001). IOP Publishing

  19. Kaur G, Singh N, Kumar M (2023) Image Forgery techniques: a review. Artif Intell Rev 56:1577–1625

  20. Fridrich J, Kodovsky J, Lukas J (2013) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7(3):868–882

    Article  Google Scholar 

  21. Bayram S, Sencar HT (2015) A survey of copy-move forgery detection methods. J Electron Imaging 24(4):041101

    Google Scholar 

  22. Gupta A, Singh G (2017) Image forgery detection techniques: a comprehensive review. Digit Invest 21:37–54

    Google Scholar 

  23. Al-Fatlawi AA, Ibrahim R (2019) Image forgery detection techniques: a review. J Telecommun Electron Comput Eng 11(2–3):45–49

    Google Scholar 

  24. Cheddad A, Condell J, Curran K, Mc Kevitt P (2010) A survey of passive techniques for digital image forensics. IEEE Trans Inf Forensics Secur 5(4):1–20

    Google Scholar 

  25. Hussain M, Muhammad K, Ali T, Afzal MK (2018) Image forgery detection techniques: a review. J Ambient Intell Humaniz Comput 9(2):395–416

    Google Scholar 

  26. Das D, Bhowmik MK, Nasipuri M (2019) A survey on image forensics techniques and applications. J Vis Commun Image Represent 59:243–267

    Google Scholar 

  27. Chierchia G, Poggi G, Sansone C (2014) A comprehensive survey on vision-based techniques for grayscale and color image forgery detection. ACM-CSUR 47(4):63

    Google Scholar 

  28. Agarwal A, Kumar V, Gupta P (2017) Image forgery detection techniques: a comprehensive review. J Electr Eng Autom 1(1):1–12

    Google Scholar 

  29. Roy S, Das D, Nasipuri M (2018) Digital image forgery detection using visual descriptors: a comprehensive survey. Multimed Tools Appl 77(8):9861–9892

    Google Scholar 

Download references

Funding

None.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, IMS Engineering College, Ghaziabad, 201009, India

    Prabhat Kumar Srivastava & Vikram Bali

  2. Department of Electronics and Communication Engineering, Feroze Gandhi Institute of Engineering and Technology, Raebareli, 229316, India

    Ghanshyam Singh

  3. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India

    Sachin Kumar

  4. Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, 473226, India

    Neelesh Kumar Jain

Authors
  1. Prabhat Kumar Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ghanshyam Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sachin Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Neelesh Kumar Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vikram Bali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sachin Kumar.

Ethics declarations

Conflicts of interest/Competing interests

The authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Srivastava, P.K., Singh, G., Kumar, S. et al. Gabor Filter and Centre Symmetric-Local Binary Pattern based technique for forgery detection in images. Multimed Tools Appl 83, 50157–50195 (2024). https://doi.org/10.1007/s11042-023-17485-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17485-1

Keywords

Search

Navigation