A Deep Learning Model to Inspect Image Forgery on SURF Keypoints of SLIC Segmented Regions


  • Uliyan Diaa Department of Information Security, College of Computer Science and Engineering, University of Ha'il, Saudi Arabia
Volume: 14 | Issue: 1 | Pages: 12549-12555 | February 2024 | https://doi.org/10.48084/etasr.6622


Copy-Move Forgery (CMF) is a common form of image manipulation attack that involves copying and pasting a part of an image to another position within the same image. This study proposes a Deep Learning (DL) model for detecting CMF, particularly in the presence of various malicious attacks. The proposed approach involves several steps, including converting the input image to grayscale, preprocessing the image using the Simple Linear Iterative Clustering (SLIC) algorithm to generate superpixel partitions, and then extracting keypoint features using the Speeded Up Robust Features (SURF) detector. Finally, a Generative Adversarial Network (GAN) is employed for feature description and matching. To assess the effectiveness of the approach, the types of features used for copy-move forgery were addressed. The proposed approach was examined under rotation, blurring, jpg compression, and scaling attacks. Furthermore, experimental results showed that the proposed approach can detect multiple CMFs with high accuracy. Finally, the proposed method was compared with recent state-of-the-art methods.


SURF key points, SLIC segmentation, image forgery, deep learning


Download data is not yet available.


A. Mahfuth, S. Yussof, A. A. Baker, and N. Ali, "A systematic literature review: Information security culture," in 2017 International Conference on Research and Innovation in Information Systems (ICRIIS), Langkawi, Malaysia, Jul. 2017, pp. 1–6.

H. Wu, J. Zhou, J. Tian, and J. Liu, "Robust Image Forgery Detection over Online Social Network Shared Images," in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, Jun. 2022, pp. 13430–13439.

M. Ali Qureshi and M. Deriche, "A review on copy move image forgery detection techniques," in 2014 IEEE 11th International Multi-Conference on Systems, Signals & Devices (SSD14), Barcelona, Spain, Oct. 2014, pp. 1–5.

H. G. Zaini, "Image Segmentation to Secure LSB2 Data Steganography," Engineering, Technology & Applied Science Research, vol. 11, no. 1, pp. 6632–6636, Feb. 2021.

A. Munshi, "Randomly-based Stepwise Multi-Level Distributed Medical Image Steganography," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10922–10930, Jun. 2023.

S. Gupta and N. Mohan, "Color Channel Characteristics (CCC) for Efficient Digital Image Forensics," Engineering, Technology & Applied Science Research, vol. 8, no. 1, pp. 2555–2561, Feb. 2018.

J. Fridrich, D. Soukal, and J. Lukas, "Detection of copy-move forgery in digital images," in Proceedings of Digital Forensic Research Workshop, 2003, vol. 3, no. 2, pp. 652–63.

A. D. Warbhe, R. V. Dharaskar, and V. M. Thakare, "A Survey on Keypoint Based Copy-paste Forgery Detection Techniques," Procedia Computer Science, vol. 78, pp. 61–67, Jan. 2016.

B. Xiao, Y. Wei, X. Bi, W. Li, and J. Ma, "Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering," Information Sciences, vol. 511, pp. 172–191, Feb. 2020.

S. Sadeghi, S. Dadkhah, H. A. Jalab, G. Mazzola, and D. Uliyan, "State of the art in passive digital image forgery detection: copy-move image forgery," Pattern Analysis and Applications, vol. 21, no. 2, pp. 291–306, May 2018.

Y. Guo, X. Cao, W. Zhang, and R. Wang, "Fake Colorized Image Detection," IEEE Transactions on Information Forensics and Security, vol. 13, no. 8, pp. 1932–1944, Dec. 2018.

T. Thakur, K. Singh, and A. Yadav, "Blind Approach for Digital Image Forgery Detection," International Journal of Computer Applications, vol. 179, no. 10, pp. 34–42, Jan. 2018.

H. Bay, T. Tuytelaars, and L. Van Gool, "SURF: Speeded Up Robust Features," in Computer Vision – ECCV 2006, Graz, Austria, 2006, pp. 404–417.

M. Verma and D. Singh, "Survey on image copy-move forgery detection," Multimedia Tools and Applications, Aug. 2023.

X. Wang, X. Wang, P. Niu, and H. Yang, "Accurate and robust image copy-move forgery detection using adaptive keypoints and FQGPCET-GLCM feature," Multimedia Tools and Applications, May 2023.

B. Yang, X. Sun, H. Guo, Z. Xia, and X. Chen, "A copy-move forgery detection method based on CMFD-SIFT," Multimedia Tools and Applications, vol. 77, no. 1, pp. 837–855, Jan. 2018.

J. Zhong, Y. Gan, J. Young, L. Huang, and P. Lin, "A new block-based method for copy move forgery detection under image geometric transforms," Multimedia Tools and Applications, vol. 76, no. 13, pp. 14887–14903, Jul. 2017.

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, Jan. 2021, Art. no. 102966.

J. Li, X. Li, B. Yang, and X. Sun, "Segmentation-Based Image Copy-Move Forgery Detection Scheme," IEEE Transactions on Information Forensics and Security, vol. 10, no. 3, pp. 507–518, Mar. 2015.

N. Goel, S. Kaur, and R. Bala, "Dual branch convolutional neural network for copy move forgery detection," IET Image Processing, vol. 15, no. 3, pp. 656–665, 2021.

X. Wang, H. Wang, S. Niu, and J. Zhang, "Detection and localization of image forgeries using improved mask regional convolutional neural network," Mathematical Biosciences and Engineering, vol. 16, no. 5, pp. 4581–4593, 2019.

Y. Rodriguez-Ortega, D. M. Ballesteros, and D. Renza, "Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics," Journal of Imaging, vol. 7, no. 3, Mar. 2021, Art. no. 59.

Q. Li, C. Wang, X. Zhou, and Z. Qin, "Image copy-move forgery detection and localization based on super-BPD segmentation and DCNN," Scientific Reports, vol. 12, no. 1, Sep. 2022, Art. no. 14987.

C. Y. Ren and I. Reid, "gSLIC: a real-time implementation of SLIC superpixel segmentation," University of Oxford, UK, Jun. 2011.

A. Likas, N. Vlassis, and J. J. Verbeek, "The global k-means clustering algorithm," Pattern Recognition, vol. 36, no. 2, pp. 451–461, Feb. 2003.

D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, Nov. 2004.

I. Goodfellow et al., "Generative adversarial networks," Communications of the ACM, vol. 63, no. 11, pp. 139–144, Oct. 2020.

D. Tralic, I. Zupancic, S. Grgic, and M. Grgic, "CoMoFoD — New database for copy-move forgery detection," in Proceedings ELMAR-2013, Zadar, Croatia, Sep. 2013, pp. 49–54, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6658316.

I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, and G. Serra, "A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery," IEEE Transactions on Information Forensics and Security, vol. 6, no. 3, pp. 1099–1110, Sep. 2011.

M. M. A. Alhaidery, A. H. Taherinia, and H. I. Shahadi, "A robust detection and localization technique for copy-move forgery in digital images," Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 1, pp. 449–461, Jan. 2023.

T. Nazir, M. Nawaz, M. Masood, and A. Javed, "Copy move forgery detection and segmentation using improved mask region-based convolution network (RCNN)," Applied Soft Computing, vol. 131, Dec. 2022, Art. no. 109778.

Y. Wu, W. Abd-Almageed, and P. Natarajan, "BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization," in Computer Vision – ECCV 2018, Munich, Germany, 2018, pp. 170–186.


How to Cite

U. Diaa, “A Deep Learning Model to Inspect Image Forgery on SURF Keypoints of SLIC Segmented Regions”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 1, pp. 12549–12555, Feb. 2024.


Abstract Views: 340
PDF Downloads: 383

Metrics Information