Hybrid SVD and SURF-Based Framework for Robust Image Forgery Detection and Object Localization
DOI:
https://doi.org/10.18196/jrc.v6i2.25567Keywords:
Copy-Move Forgery, Forged Object Localization, Image Processing, SURF, SVDAbstract
This paper presents a highly effective and reliable approach for detecting image forgery and identifying manipulated regions in digital images. The proposed method uses a combination of Singular Value Decomposition (SVD) and the Speeded-Up Robust Features (SURF) algorithm, achieving a high degree accuracy of 99.1% for revealed tampering. After an input image is initially divided parallel to partition, then is performed by SVD to extract features with remarkable discriminability, the method is valued based on independent experiments. The norms are calculated, and pixels with the same norm begin to group to identify potentially tampered areas. In order to simplify the detection process, we conduct a weighted comparison among subgroups to distinguish real structures from false ones. Once we discover a suspicious forgery area, the SURF algorithm comes into play to accurately identify the manipulated items. This process uses a keypoint detector, descriptor calculations, the match between points, and geometric checking to improve the accuracy and reliability of forgery localization. Experimental results on different image databases show that this method is effective. It exhibits advanced ability in detecting forgeries, finding objects and locating where they are in an image. Eventually, we hope this work will produce a sturdy forgery detection system and improve the accuracy of recognizing tampered regions. The proposed method is useful in digital forensics and image verification.
References
M. H. Farhan, K. Shaker, and S. Al-Janabi, "Copy–move forgery detection in digital image forensics: A survey," Multimedia Tools and Applications, pp. 1-33, 2024.
M. H. Farhan, K. Shaker, and S. Al-Janabi, "Double Dual Convolutional Neural Network (D2CNN) for Copy-Move Forgery Detection," in 2023 15th International Conference on Developments in eSystems Engineering (DeSE), pp. 439-444, 2023.
C. Bhure, G. S. Nicholas, S. Ghosh, N. Asadi, and F. Saqib, "AutoDetect: Novel Autoencoding Architecture for Counterfeit IC Detection," Journal of Hardware and Systems Security, pp. 1-20, 2024.
S. K. Narasimhamurthy, V. K. Mahadevachar, and R. K. T. Narasimhamurthy, "A Copy-Move Image Forgery Detection Using Modified SURF Features and AKAZE Detector," International Journal of Intelligent Engineering & Systems, vol. 16, no. 4, 2023.
J. E. Kennedy, "Addressing researcher fraud: retrospective, real-time, and preventive strategies–including legal points and data management that prevents fraud," Frontiers in Research Metrics and Analytics, vol. 9, p. 1397649, 2024.
A. Islam, C. Long, A. Basharat, and A. Hoogs, "Doa-gan: Dual-order attentive generative adversarial network for image copy-move forgery detection and localization," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 4676-4685, 2020.
M. Saddique, K. Asghar, U. I. Bajwa, M. Hussain, and Z. Habib, "Spatial Video Forgery Detection and Localization using Texture Analysis of Consecutive Frames," Advances in Electrical & Computer Engineering, vol. 19, no. 3, 2019.
R. G. Mani, R. Parthasarathy, S. Eswaran, and P. Honnavalli, "A Survey on Digital Image Forensics: Metadata and Image forgeries," in Workshop on Applied Computing, January 27-28, 22, 2022, vol. 55, pp. 22-55.
Z. N. Khudhair, F. Mohamed, and K. A. Kadhim, "A review on copy-move image forgery detection techniques," in Journal of Physics: Conference Series, vol. 1892, no. 1, p. 012010, 2021.
A. S. Al-Qazzaz, P. Salehpour, and H. S. Aghdasi, "Robust DeepFake Face Detection Leveraging Xception Model and Novel Snake Optimization Technique," Journal of Robotics and Control (JRC), vol. 5, no. 5, pp. 1444-1456, 2024.
M. Qadir, S. Tehsin, and S. Kausar, "Detection of Copy Move Forgery in Medical Images Using Deep Learning," in 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), pp. 1-6, 2021.
Z. N. Khudhair, F. Mohamed, A. Rehman, and T. Saba, "Detection of Copy-Move Forgery in Digital Images Using Singular Value Decomposition," Computers, Materials & Continua, vol. 74, no. 2, 2023.
Y. Wu, W. Abd-Almageed, and P. Natarajan, "Image copy-move forgery detection via an end-to-end deep neural network," in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1907-1915, 2018.
K. K. Thyagharajan and G. Kalaiarasi, "Pulse coupled neural network based near-duplicate detection of images (PCNN–NDD)," Advances in Electrical and Computer Engineering, vol. 18, no. 3, pp. 87-96, 2018.
P. Deb, S. Deb, A. Das, and N. Kar, "Image Forgery Detection Techniques: Latest Trends And Key Challenges," IEEE Access, vol. 12, pp. 169452-169466, 2024.
X.-y. Wang, C. Wang, L. Wang, H.-y. Yang, and P.-p. Niu, "Robust and effective multiple copy-move forgeries detection and localization," Pattern Analysis and Applications, vol. 24, pp. 1025-1046, 2021.
S. U. Qureshi et al., "Systematic review of deep learning solutions for malware detection and forensic analysis in IoT," Journal of King Saud University-Computer and Information Sciences, p. 102164, 2024.
A. Ghai, P. Kumar, and S. Gupta, "A deep-learning-based image forgery detection framework for controlling the spread of misinformation," Information Technology & People, vol. 37, no. 2, pp. 966-997, 2024.
K. Asghar, Z. Habib, and M. Hussain, "Copy-move and splicing image forgery detection and localization techniques: a review," Australian Journal of Forensic Sciences, vol. 49, no. 3, pp. 281-307, 2017.
K. Asghar, M. Saddique, M. Hussain, G. Bebis, and Z. Habib, "Image Forgery Detection Using Noise and Edge Weighted Local Texture Features," Advances in Electrical and Computer Engineering, vol. 22, no. 1, pp. 57-68, 2022.
M. SaberiKamarposhti, A. Ghorbani, and M. Yadollahi, "A comprehensive survey on image encryption: Taxonomy, challenges, and future directions," Chaos, Solitons & Fractals, vol. 178, p. 114361, 2024.
C. B. Rabah. Analysis of scanned documents for integrity and authenticity checking. (Doctoral dissertation, Ecole nationale supérieure Mines-Télécom Atlantique; École supérieure des communications de Tunis (Tunisie)), 2021.
P. Aberna and L. Agilandeeswari, "Digital image and video watermarking: methodologies, attacks, applications, and future directions," Multimedia Tools and Applications, vol. 83, no. 2, pp. 5531-5591, 2024.
S. Mukherjee and A. K. Pal, "A hybrid SWT-SVD based multiresolution features for robust image copy-move forgery detection," Multimedia Tools and Applications, vol. 83, no. 16, pp. 48141-48163, 2024.
P. M. Raju and M. S. Nair, "Copy-move forgery detection using binary discriminant features," Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 2, pp. 165-178, 2022.
X.-y. Wang, X.-q. Wang, P.-p. Niu, and H.-y. Yang, "Accurate and robust image copy-move forgery detection using adaptive keypoints and FQGPCET-GLCM feature," Multimedia Tools and Applications, vol. 83, no. 1, pp. 2203-2235, 2024.
N. Kaur, N. Jindal, and K. Singh, "A deep learning framework for copy-move forgery detection in digital images," Multimedia Tools and Applications, vol. 82, no. 12, pp. 17741-17768, 2023.
S. Kumar, S. Mukherjee, and A. K. Pal, "An improved reduced feature-based copy-move forgery detection technique," Multimedia Tools and Applications, vol. 82, no. 1, pp. 1431-1456, 2023.
D. P. Timothy and A. K. Santra, "Detecting Digital Image Forgeries with Copy-Move and Splicing Image Analysis using Deep Learning Techniques," International Journal of Advanced Computer Science & Applications, vol. 15, no. 5, 2024.
A. Diwan, R. Mahadeva, and V. Gupta, "Advancing Copy-Move Manipulation Detection in Complex Image Scenarios Through Multiscale Detector," IEEE Access, vol. 12, pp. 64736-64753, 2024.
A. Yadav, J. Goyal, and M. Ahmed, "Robust block-based watermarking algorithm with parallelization using multi-level discrete wavelet transformation," Journal of Real-Time Image Processing, vol. 21, no. 6, p. 182, 2024.
S. A. Hosseini and P. Farahmand, "An attack resistant hybrid blind image watermarking scheme based on combination of DWT, DCT and PCA," Multimedia Tools and Applications, vol. 83, no. 7, pp. 18829-18852, 2024.
D. Pennati and L. Bocchi, "Analysis of the Relationship Between Scale Invariant Feature Transform Keypoint Properties and Their Invariance to Geometrical Transformation Applied to Cone-Beam Computed Tomography Images," Bioengineering, vol. 11, no. 12, p. 1236, 2024.
U. Diaa, "A Deep Learning Model to Inspect Image Forgery on SURF Keypoints of SLIC Segmented Regions," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12549-12555, 2024.
M. M. Eltoukhy, F. S. Alsubaei, A. M. Mortda, and K. M. Hosny, "An efficient convolution neural network method for copy-move video forgery detection," Alexandria Engineering Journal, vol. 110, pp. 429-437, 2025.
Z. Ullah, L. Qi, E. Pires, A. Reis, and R. R. Nunes, "A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts," Computers, Materials & Continua, vol. 80, no. 2, 2024.
F. Usman, C. Shi, and Y. Wang, "DRL-SLAM: Enhanced Object Detection Fusion with Improved YOLOv8," in International Conference on Intelligence Science, pp. 257-272, 2024.
M. Verma and D. Singh, "Survey on image copy-move forgery detection," Multimedia Tools and Applications, vol. 83, no. 8, pp. 23761-23797, 2024.
M. F. Ab Jabal, A. N. H. Abdullah, F. H. Najjar, S. Hamid, A. K. Khalid, and W. D. W. A. Manan, "A Novel Color Feature for the Improvement of Pigment Spot Extraction in Iris Images," Journal of Image and Graphics, vol. 12, no. 4, 2024.
U. Samariya, S. D. Kamble, S. Singh, and R. K. Sonker, "A survey on copy-move image forgery detection based on deep-learning techniques," Multimedia Tools and Applications, pp. 1-60, 2024.
A. Bilal et al., "Improved Support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification," Plos one, vol. 19, no. 1, p. e0295951, 2024.
A. Pyrkov, A. Aliper, D. Bezrukov, D. Podolskiy, F. Ren, and A. Zhavoronkov, "Complexity of life sciences in quantum and AI era," Wiley Interdisciplinary Reviews: Computational Molecular Science, vol. 14, no. 1, p. e1701, 2024.
Z. N. Khudhair et al., "Color to Grayscale Image Conversion Based on Singular Value Decomposition," IEEE Access, vol. 11, pp. 54629-54638, 2023.
F. A. Khattak, I. K. Proudler, and S. Weiss, "Scalable analytic eigenvalue extraction algorithm," IEEE Access, vol. 12, pp. 166652-166659, 2024.
N. K. El Abbadi, A. Al Rammahi, D. S. Redha, and M. AbdulHameed, "Image Compression based on SVD and MPQ-BTC," J. Comput. Sci., vol. 10, no. 10, pp. 2095-2104, 2014.
N. K. El Abbadi and E. J. Al Taee, "An Efficient Storage Format for Large Sparse Matrices based on Quadtree," International Journal of Computer Applications, vol. 105, no. 13, 2014.
N. K. El Abbadi, A. Mohamad, and M. Abdul-Hameed, "Image encryption based on singular value decomposition," Journal of Computer Science, vol. 10, no. 7, p. 1222, 2014.
A. Abdi, J.-P. Berrut, and S. Hosseini, "Explicit methods based on barycentric rational interpolants for solving non-stiff Volterra integral equations," Applied Numerical Mathematics, vol. 174, pp. 127-141, 2022.
D. Kumar, R. C. Pandey, and A. K. Mishra, "A review of image features extraction techniques and their applications in image forensic," Multimedia Tools and Applications, pp. 1-102, 2024.
H. Bay, T. Tuytelaars, and L. Van Gool, "Surf: Speeded up robust features," in Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006. Proceedings, Part I 9, pp. 404-417, 2006.
A. Kumar, "SURF feature descriptor for image analysis," Imaging and Radiation Research, vol. 6, no. 1, p. 5643, 2024.
N. K. EL Abbadi, S. A. Al Hassani, and A. H. Abdulkhaleq, "Panoramic Image Stitching Techniques Based on SURF and Singular Value Decomposition," in International Conference on New Trends in Information and Communications Technology Applications, pp. 63-86, 2021.
J. Dong, W. Wang, and T. Tan, "Casia image tampering detection evaluation database," in 2013 IEEE China summit and international conference on signal and information processing, pp. 422-426, 2013.
D. Tralic, I. Zupancic, S. Grgic, and M. Grgic, "CoMoFoD—New database for copy-move forgery detection," in Proceedings ELMAR-2013, pp. 49-54, 2013.
F. H. Najjar, N. B. Hassan, and S. Abd Kadum, "Hybrid Deep Learning Model for Hippocampal Localization in Alzheimer's Diagnosis Using U-Net and VGG16," International Journal of Robotics and Control Systems, vol. 5, no. 2, pp. 730-747, 2025.
S. M. Saadi and W. Al-Jawher, "Ensemble Learning with optimum Feature Selection for Tweet Fake News Detection using the Dragonfly approach," in 2023 16th International Conference on Developments in eSystems Engineering (DeSE), pp. 575-580, 2023.
N. I. Ali, A. H. Soomro, S. Soomro, and A. G. Memon, "RSS Feeds Filtering from Multiple Sources Using Automated Techniques of Natural Language Processing," The Asian Bulletin of Big Data Management, vol. 4, no. 1, p. 250, 2024.
F. H. Najjar, S. Abd Kadum, and N. B. Hassan, "Integrating Multi-scale Feature Extraction into EfficientNet for Acute Lymphoblastic Leukemia Classification," Journal of Image and Graphics, vol. 13, no. 1, pp. 83-89, 2025, doi: 10.18178/joig.13.1.83-89.
G. Fu, Y. Zhang, and Y. Wang, "Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering," Applied Sciences, vol. 13, no. 13, p. 7528, 2023.
N. K. Rathore, N. K. Jain, P. K. Shukla, U. Rawat, and R. Dubey, "Image forgery detection using singular value decomposition with some attacks," National Academy Science Letters, vol. 44, pp. 331-338, 2021.
B. Ahmed, T. A. Gulliver, and S. alZahir, "Blind copy-move forgery detection using SVD and KS test," SN Applied Sciences, vol. 2, no. 8, p. 1377, 2020.
S. Tinnathi and G. Sudhavani, "An efficient copy move forgery detection using adaptive watershed segmentation with AGSO and hybrid feature extraction," Journal of Visual Communication and Image Representation, vol. 74, p. 102966, 2021.
P.-p. Niu, C. Wang, W. Chen, H. Yang, and X. Wang, "Fast and effective Keypoint-based image copy-move forgery detection using complex-valued moment invariants," Journal of Visual Communication and Image Representation, vol. 77, p. 103068, 2021.
K. B. Meena and V. Tyagi, "A hybrid copy-move image forgery detection technique based on Fourier-Mellin and scale invariant feature transforms," Multimedia Tools and Applications, vol. 79, no. 11, pp. 8197-8212, 2020.
J. Wang et al., "Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining," in Companion Proceedings of the ACM on Web Conference 2024, 2024, pp. 943-946.
Priyanka, G. Singh, and K. Singh, "An improved block based copy-move forgery detection technique," Multimedia Tools and Applications, vol. 79, pp. 13011-13035, 2020.
A. Diwan and A. K. Roy, "CNN-Keypoint Based Two-Stage Hybrid Approach for Copy-Move Forgery Detection," IEEE Access, vol. 12, pp. 43809-43826, 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Fallah H. Najjar, Ansam Ali AbdulAmeer , Salman Abd Kadum
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This journal is based on the work at https://journal.umy.ac.id/index.php/jrc under license from Creative Commons Attribution-ShareAlike 4.0 International License. You are free to:
- Share – copy and redistribute the material in any medium or format.
- Adapt – remix, transform, and build upon the material for any purpose, even comercially.
The licensor cannot revoke these freedoms as long as you follow the license terms, which include the following:
- Attribution. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions. You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
• Creative Commons Attribution-ShareAlike (CC BY-SA)
JRC is licensed under an International License
