Fast and Robust Copy-Move Forgery Detection Using BRIEF, FAST, and SIFT Feature Matching

Muhammad Jamshed Abbass; Ali Waqar; Natasha Seemab; Abdul Saboor Khan; Muhammad Bilal Riaz; Sharjeel Abbas; Bilal Mushtaq

doi:10.62762/TETAI.2025.152706

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Volume 3, Issue 1, ICCK Transactions on Emerging Topics in Artificial Intelligence

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ICCK Transactions on Emerging Topics in Artificial Intelligence, Volume 3, Issue 1, 2026: 9-19

Open Access | Research Article | 25 November 2025

Fast and Robust Copy-Move Forgery Detection Using BRIEF, FAST, and SIFT Feature Matching

Muhammad Jamshed Abbass 1

Ali Waqar 2

Natasha Seemab 3

Abdul Saboor Khan 4

Muhammad Bilal Riaz 5

Sharjeel Abbas 6

Bilal Mushtaq 7 *

1 Faculty of Electrical Engineering, Wrocław University of Science and Technology, 50-370 Wrocław, Poland

2 School of Mechanical and Electrical Engineering, Quanzhou University of Information Engineering, Quanzhou 362000, China

3 Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand

4 Department of Electrical Engineering and Information Technology, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany

5 IT4Innovations, VSB – Technical University of Ostrava, 708 00 Ostrava, Czech Republic

6 Faculty of Electrical Engineering, Saint Petersburg Electrotechnical University "LETI", 197376, Saint Petersburg, Russia

7 Electrical and Electronic Engineering Department, Beaconhouse International College, Islamabad 44000, Pakistan

* Corresponding Author: Bilal Mushtaq, [email protected]

DOI: 10.62762/TETAI.2025.152706

Received: 11 July 2025, Accepted: 02 October 2025, Published: 25 November 2025

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Abstract

This paper presents a novel hybrid copy–move forgery detection method that combines the efficiency of FAST-BRIEF (for rapid keypoint detection and binary descriptors) with the robustness of SIFT (for scale- and rotation-invariant feature matching). The proposed framework employs g2NN matching for accurate feature correspondence, followed by morphological processing and LSC-SSIM superpixel segmentation for precise localization of tampered regions. The method is evaluated on 30 diverse test images from benchmark datasets comprising over 700 images, achieving a 95% F-measure with an average CPU time of 6.02 seconds. It demonstrates strong resilience to geometric transformations (rotation, scaling), photometric adjustments (contrast, brightness), additive noise, and multiple forgeries. The proposed methodology offers a 5–30% improvement in accuracy and computational speed. This approach addresses emerging challenges in deepfake detection and satellite imagery authentication, where localized manipulations threaten media integrity.

Graphical Abstract

Fast and Robust Copy-Move Forgery Detection Using BRIEF, FAST, and SIFT Feature Matching

Keywords

hybrid FAST-BRIEF-SIFT

g2NN matching

LSC-SSIM segmentation

copy-move forgery detection

deepfake localization

satellite image authentication

real-time forensics

f-measure optimization

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

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APA Style

Abbass, M. J., Waqar, A., Seemab, N., Khan, A. S., Riaz, M. B., Abbas, S., & Mushtaq, B. (2025). Fast and Robust Copy-Move Forgery Detection Using BRIEF, FAST, and SIFT Feature Matching. ICCK Transactions on Emerging Topics in Artificial Intelligence, 3(1), 9–19. https://doi.org/10.62762/TETAI.2025.152706

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TY  - JOUR
AU  - Abbass, Muhammad Jamshed
AU  - Waqar, Ali
AU  - Seemab, Natasha
AU  - Khan, Abdul Saboor
AU  - Riaz, Muhammad Bilal
AU  - Abbas, Sharjeel
AU  - Mushtaq, Bilal
PY  - 2025
DA  - 2025/11/25
TI  - Fast and Robust Copy-Move Forgery Detection Using BRIEF, FAST, and SIFT Feature Matching
JO  - ICCK Transactions on Emerging Topics in Artificial Intelligence
T2  - ICCK Transactions on Emerging Topics in Artificial Intelligence
JF  - ICCK Transactions on Emerging Topics in Artificial Intelligence
VL  - 3
IS  - 1
SP  - 9
EP  - 19
DO  - 10.62762/TETAI.2025.152706
UR  - https://www.icck.org/article/abs/TETAI.2025.152706
KW  - hybrid FAST-BRIEF-SIFT
KW  - g2NN matching
KW  - LSC-SSIM segmentation
KW  - copy-move forgery detection
KW  - deepfake localization
KW  - satellite image authentication
KW  - real-time forensics
KW  - f-measure optimization
AB  - This paper presents a novel hybrid copy–move forgery detection method that combines the efficiency of FAST-BRIEF (for rapid keypoint detection and binary descriptors) with the robustness of SIFT (for scale- and rotation-invariant feature matching). The proposed framework employs g2NN matching for accurate feature correspondence, followed by morphological processing and LSC-SSIM superpixel segmentation for precise localization of tampered regions. The method is evaluated on 30 diverse test images from benchmark datasets comprising over 700 images, achieving a 95% F-measure with an average CPU time of 6.02 seconds. It demonstrates strong resilience to geometric transformations (rotation, scaling), photometric adjustments (contrast, brightness), additive noise, and multiple forgeries. The proposed methodology offers a 5–30% improvement in accuracy and computational speed. This approach addresses emerging challenges in deepfake detection and satellite imagery authentication, where localized manipulations threaten media integrity.
SN  - 3068-6652
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Abbass2025Fast,
  author = {Muhammad Jamshed Abbass and Ali Waqar and Natasha Seemab and Abdul Saboor Khan and Muhammad Bilal Riaz and Sharjeel Abbas and Bilal Mushtaq},
  title = {Fast and Robust Copy-Move Forgery Detection Using BRIEF, FAST, and SIFT Feature Matching},
  journal = {ICCK Transactions on Emerging Topics in Artificial Intelligence},
  year = {2025},
  volume = {3},
  number = {1},
  pages = {9-19},
  doi = {10.62762/TETAI.2025.152706},
  url = {https://www.icck.org/article/abs/TETAI.2025.152706},
  abstract = {This paper presents a novel hybrid copy–move forgery detection method that combines the efficiency of FAST-BRIEF (for rapid keypoint detection and binary descriptors) with the robustness of SIFT (for scale- and rotation-invariant feature matching). The proposed framework employs g2NN matching for accurate feature correspondence, followed by morphological processing and LSC-SSIM superpixel segmentation for precise localization of tampered regions. The method is evaluated on 30 diverse test images from benchmark datasets comprising over 700 images, achieving a 95\% F-measure with an average CPU time of 6.02 seconds. It demonstrates strong resilience to geometric transformations (rotation, scaling), photometric adjustments (contrast, brightness), additive noise, and multiple forgeries. The proposed methodology offers a 5–30\% improvement in accuracy and computational speed. This approach addresses emerging challenges in deepfake detection and satellite imagery authentication, where localized manipulations threaten media integrity.},
  keywords = {hybrid FAST-BRIEF-SIFT, g2NN matching, LSC-SSIM segmentation, copy-move forgery detection, deepfake localization, satellite image authentication, real-time forensics, f-measure optimization},
  issn = {3068-6652},
  publisher = {Institute of Central Computation and Knowledge}
}

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