Image Splicing Forgery Detection using Standard Division-Local Binary Pattern Features
DOI:
https://doi.org/10.25007/ajnu.v12n3a1839Keywords:
Splicing Image, Forgery detection, Texture features, Artificial neural network, Image ProcessingAbstract
Numerous aspects of daily life contribute to societal stability, and the security of people's perceptions of the world online is one target of various malicious attacks. Professional forgers can now quickly create copy-move, splice, or retouch photos with the use of today's advanced tools. It has been determined that splicing, is a widespread method of manipulating images. Image forgery can also lead to substantial setbacks and challenges, some of which may have significant ethical, moral, or legal consequences. Thus, the paper proposes a system that combines SD-LBP (Standard Devision-Local Binary Pattern) based passive picture splicing detection system and ANN classifier. The SD-LBP is created to have the benefits and avoid drawbacks of Local Binary Pattern (LBP). The SD-LBP extraction is typically performed by employing proposed SD value-based thresholding instead of the center pixel, which is robust to noise and other photometric attacks. The second part of the proposed system is the ANN classifier is used that extract the feature of images to lower the error and build a model that can tell spliced images from real photos that have been digitally altered. The proposed system is creating a reliable image forgery detection technique that was implemented with CASIA V2.0 standard dataset. The results showing that it outperformed compared with other methods on the in terms of accuracy (97.8%), sensitivity (98.6%), and specificity (97.1%). Most importantly, the proposed SFD method exceeded the state-of-the-art efforts in this field in terms of accuracy.
Downloads
References
X. Bi, Z. Zhang, and B. Xiao, “Reality transform adversarial generators for image splicing forgery detection and localization,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 14294–14303.
M. A. Qureshi and M. Deriche, “A bibliography of pixel-based blind image forgery detection techniques,” Signal Process Image Commun, vol. 39, pp. 46–74, 2015.
H. Yao, S. Wang, X. Zhang, C. Qin, and J. Wang, “Detecting Image Splicing Based on Noise Level Inconsistency,” Multimed Tools Appl, vol. 76, no. 10, pp. 12457–12479, 2017, doi: 10.1007/s11042-016-3660-3.
Y. Li and S. Lyu, “Obstructing DeepFakes by Disrupting Face Detection and Facial Landmarks Extraction,” Deep Learning-Based Face Analytics, pp. 247–267, 2021.
E. I. A. El-Latif, A. Taha, and H. H. Zayed, “A passive approach for detecting image splicing using deep learning and haar wavelet transform,” International Journal of Computer Network and Information Security, vol. 11, no. 5, pp. 28–35, 2019.
Y. Rao, J. Ni, and H. Zhao, “Deep learning local descriptor for image splicing detection and localization,” IEEE access, vol. 8, pp. 25611–25625, 2020.
R. W. Ibrahim, H. A. Jalab, F. K. Karim, E. Alabdulkreem, and M. N. Ayub, “A medical image enhancement based on generalized class of fractional partial differential equations,” Quant Imaging Med Surg, vol. 12, no. 1, p. 172, 2022.
A. Raghunandan, P. Raghav, and H. V. R. Aradhya, “Object detection algorithms for video surveillance applications,” in 2018 International Conference on Communication and Signal Processing (ICCSP), IEEE, 2018, pp. 563–568.
K. Bahrami, A. C. Kot, L. Li, and H. Li, “Blurred image splicing localization by exposing blur type inconsistency,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 5, pp. 999–1009, 2015.
X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” IEEE transactions on image processing, vol. 19, no. 6, pp. 1635–1650, 2010.
Z. Guo, L. Zhang, and D. Zhang, “A completed modeling of local binary pattern operator for texture classification,” IEEE transactions on image processing, vol. 19, no. 6, pp. 1657–1663, 2010.
G. Muhammad, M. H. Al-Hammadi, M. Hussain, and G. Bebis, “Image forgery detection using steerable pyramid transform and local binary pattern,” Mach Vis Appl, vol. 25, pp. 985–995, 2014.
M. Zandi, A. Mahmoudi-Aznaveh, and A. Mansouri, “Adaptive matching for copy-move forgery detection,” in 2014 IEEE international workshop on information forensics and security (WIFS), IEEE, 2014, pp. 119–124.
I. Aizenberg, T. Bregin, C. Butakoff, V. Karnaukhov, N. Merzlyakov, and O. Milukova, “Type of blur and blur parameters identification using neural network and its application to image restoration,” in ICANN, 2002, pp. 1231–1236.
J. A. Ahmed and A. M. A. Brifcani, “A new internal architecture based on feature selection for holonic manufacturing system,” International Journal of Industrial and Manufacturing Engineering, vol. 9, no. 8, pp. 1549–1552, 2015.
C. Li, Q. Ma, L. Xiao, M. Li, and A. Zhang, “Image splicing detection based on Markov features in QDCT domain,” Neurocomputing, vol. 228, pp. 29–36, 2017.
C. Li, Q. Ma, L. Xiao, M. Li, and A. Zhang, “Image splicing detection based on Markov features in QDCT domain,” Neurocomputing, vol. 228, pp. 29–36, 2017.
A. Alahmadi, M. Hussain, H. Aboalsamh, G. Muhammad, G. Bebis, and H. Mathkour, “Passive detection of image forgery using DCT and local binary pattern,” Signal Image Video Process, vol. 11, no. 1, pp. 81–88, 2017.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Academic Journal of Nawroz University
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors retain copyright
The use of a Creative Commons License enables authors/editors to retain copyright to their work. Publications can be reused and redistributed as long as the original author is correctly attributed.
- Copyright
- The researcher(s), whether a single or joint research paper, must sell and transfer to the publisher (the Academic Journal of Nawroz University) through all the duration of the publication which starts from the date of entering this Agreement into force, the exclusive rights of the research paper/article. These rights include the translation, reuse of papers/articles, transmit or distribute, or use the material or parts(s) contained therein to be published in scientific, academic, technical, professional journals or any other periodicals including any other works derived from them, all over the world, in English and Arabic, whether in print or in electronic edition of such journals and periodicals in all types of media or formats now or that may exist in the future. Rights also include giving license (or granting permission) to a third party to use the materials and any other works derived from them and publish them in such journals and periodicals all over the world. Transfer right under this Agreement includes the right to modify such materials to be used with computer systems and software, or to reproduce or publish it in e-formats and also to incorporate them into retrieval systems.
- Reproduction, reference, transmission, distribution or any other use of the content, or any parts of the subjects included in that content in any manner permitted by this Agreement, must be accompanied by mentioning the source which is (the Academic Journal of Nawroz University) and the publisher in addition to the title of the article, the name of the author (or co-authors), journal’s name, volume or issue, publisher's copyright, and publication year.
- The Academic Journal of Nawroz University reserves all rights to publish research papers/articles issued under a “Creative Commons License (CC BY-NC-ND 4.0) which permits unrestricted use, distribution, and reproduction of the paper/article by any means, provided that the original work is correctly cited.
- Reservation of Rights
The researcher(s) preserves all intellectual property rights (except for the one transferred to the publisher under this Agreement).
- Researcher’s guarantee
The researcher(s) hereby guarantees that the content of the paper/article is original. It has been submitted only to the Academic Journal of Nawroz University and has not been previously published by any other party.
In the event that the paper/article is written jointly with other researchers, the researcher guarantees that he/she has informed the other co-authors about the terms of this agreement, as well as obtaining their signature or written permission to sign on their behalf.
The author further guarantees:
- The research paper/article does not contain any defamatory statements or illegal comments.
- The research paper/article does not violate other's rights (including but not limited to copyright, patent, and trademark rights).
This research paper/article does not contain any facts or instructions that could cause damages or harm to others, and publishing it does not lead to disclosure of any confidential information.
