Effect of Image Splicing and Copy Move Forgery on Haralick Features of a Digital Image


  • Nura Sulaiman Department of Computer Science, Ahmadu Bello UniversityZaria, Nigeria
  • Mustapha Aminu Bagiwa Department of Computer Science Ahmadu Bello University Zaria, Nigeria.
  • Kasim Shafii Department of Computer Science Ahmadu Bello University Zaria, Nigeria.
  • Abubakar Usman Mohammed Department of Computer Science, Federal University Gusau,Gusau Nigeria


splicing, copy move, forgery, haralick features


Nowadays, digital images have become the fastest way of transferring information, but the existence of advanced photo-editing tools makes it easy to alter digital image content which may be used as proof in a legal case, thus creating a serious problem.  The most frequent techniques of image forgery include splicing and copy move. Splicing is an image forgery technique in which, the forger crops a part of the first image and places it in the second image while Copy move forgery is a type of image manipulation that involves copying and pasting at least one component of an image onto other areas of the same image for the purpose of duplication or removal of objects in the image. Finding the integrity of a digital image is critical since it can be used as a legal proof in a multitude of sectors, including investigation of a crime scene. Equally, finding the features of an image that change as a result of image manipulation such as copy move and splicing is very important as this can be used to distinguish between forged and original image. Therefore, in this paper, we looked upon the effect of image splicing and copy move forgery on haralick features of a digital image. CoMoFoD dataset and Images frames extracted from original and spliced videos were used in this experiment. The result of this experiment shows that splicing and copy move manipulations have no effect on haralick features of a digital image. As a result, these features cannot be used to tell if an image has been spliced or was manipulated by a copy move forgery.


Campbell, D. L., Kang, H., & Shokouhl, S. (2017). Application of Haralick texture features in brain florbetapir positron emission tomography without reference region normalization. Dovepress, 12.

Chen, C., McCloskey, S., & Yu, J. (2017). Image Splicing Detection via Camera Response Function Analysis. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 10. doi: 10.1109/CVPR.2017.203

Choras, R. S. (2007). Image feature extraction techniques and their applications for CBIR and biometrics systems. INTERNATIONAL JOURNAL OF BIOLOGY AND BIOMEDICAL ENGINEERING, 1, 1-11.

Cozzolino, D., & Verdoliva, L. (2016). Single image splicing localization through autoencoder-based anomaly detection. 2016 IEEE International Workshop on Information Forensics and Security (WIFS). doi: 10.1109/WIFS.2016.7823921

Griebenow, R. (2017). Image Splicing Detection. Paper presented at the Proceedings of the Seminar Machine Learning in Computer Vision and Natural Language Processing, University of Colorado, Colorado Springs.

Kaizhen, Z., & Zhang, Z. (2012). A Novel Algorithm Of Image Splicing Detection. 2012 International Conference on Industrial Control and Electronics Engineering, 4.

Kashyap, A., Parmar, R. S., Agarwal, M., & Gupta, H. (2017). An Evaluation of Digital Image Forgery Detection Approaches.

Manu, V. T., & Mehtre, B. M. (2015). Visual Artifacts Based Image Splicing Detection in Uncompressed Images. Paper presented at the 2015 IEEE International Conference on Computer Graphics, Vision and Information Security(CGVIS).

Muzaffer, G., & Ulutas, G. (2019). A new deep learning-based method to detection of copy-move forgery in digital images. 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT). doi: 10.1109/ebbt.2019.8741657

Niu, Y., Tondi, B., Zhao, Y., Ni, R., & Barni, M. (2021). Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clusterin. 14.

Park, T. H., Han, J. G., Moon, Y. H., & Eom, I. K. (2016). Image splicing detection based on inter-scale 2D joint characteristic function moments in wavelet domain. EURASIP Journal on Image and Video Processing, 10.

Rad, S. J. M., Tab, F. A., & Mollazade, K. (2012). Application of Imperialist Competitive Algorithm for Feature Selection: A Case Study on Bulk Rice Classification. International Journal of Computer Applications, 40(16), 8.

RAO, Y., NI, J., & ZHAO, H. (2020). Deep Learning Local Descriptor for Image Splicing Detection and Localization. IEEE Access.

RHEE, K. H. (2020). Detection of Spliced Image Forensics Using Texture Analysis of Median Filter Residual. IEEE Access.

Singh, R. D., & Aggarwal, N. (2017). Detection of upscale-crop and splicing for digital video authentication. Digital Investigation, 31-52. doi: doi: 10.1016/j.diin.2017.01.001

Sulaiman, N., Bagiwa, M. A., Aliyu, S., Shafii, K., Usman, A. M., Mohammed, S., & Abdulsalam, A. J. (2019). DETECTION AND LOCALIZATION OF SPLICING FORGERY IN DIGITAL VIDEOS USING CONVOLUTIONAL AUTO-ENCODER AND GOTURN ALGORITHM. FUDMA Journal of Sciences (FJS), 3(4), 10.

Thakur, A., & Jindal, N. (2020). Hybrid deep learning and machine learning approach for passive image forensic. IET Image Processing. doi: 10.1049/iet-ipr.2019.1291

Tralic D., Zupancic I., Grgic S., & M., G. (2013). CoMoFoD - New Database for Copy-Move Forgery Detection. 55th International Symposium ELMAR-2013.