Optimization of Copy Move Forgery Detection with Region Selection Based on Domain Specific Characteristics
Keywords:
copy move forgery, region selection optimization, deep learning signature, partially occluded counterfeitAbstract
Tampering and counterfeiting of digital images for various malicious purposes has become easier with advanced image editing tools. Copy move counterfeiting is a common image tampering technique created by copying a slice from one place to another in a image. Occlusion and partial distortions make counterfeit detection a challenge. To address it a deep learning signature approach was proposed in our earlier work. Though the accuracy of detection was high, the computational complexity was higher in that approach. This work proposes a novel region selection based optimization for reducing the computation complexity in deep learning signature approach for copy move forgery detection. The proposed region selection algorithm models the regions based on domain characteristics using a fuzzy Gaussian membership function. The proposed region selection optimization is able to reduce the computational complexity by 10% without compromising on accuracy of copy move forgery detection.
Downloads
References
Mallonee, L.(2015) Infamously Altered Photos, Before and After Their Edits. Wired.
Wikipedia contributors. (2019). List of Photo Manipulation Controversies. Wikipedia.
Allbeson, T.; Allan, S. (2019). The War of Images in the Age of Trump. In Trump’s Media War; Happer, C., Hoskins, A.,Merrin, W., Eds.; Springer International Publishing: Berlin/Heidelberg, Germany
H. Li, W. Luo, X. Qiu and J. Huang.(2017). Image Counterfeit Localization via Integrating Tampering Possibility Maps," in IEEE Transactions on Information Forensics and Security, vol. 12, no. 5, pp. 1240-1252
Y. Li and J. Zhou. (2019). Fast and Effective Image Copy-Move Counterfeit Detection via Hierarchical Feature Point Matching. in IEEE Transactions on Information Forensics and Security, vol. 14, no. 5, pp. 1307-1322
O. Mayer and M. C. Stamm.(2018). Accurate and Efficient Image Counterfeit Detection Using Lateral Chromatic Aberration. In IEEE Transactions on Information Forensics and Security, vol. 13, no. 7, pp. 1762-1777
X. Bi and C.-M. Pun.(2017). Fast reflective offset-guided searching method for copy-move forgery detection. Inf. Sci., vols. 418–419, pp. 531–545
X.-Y. Wang, S. Li, Y.-N. Liu, Y. Niu, H.-Y. Yang, and Z.-L Zhou.(2017).A new keypoint-based copy-move forgery detection for small smooth regions. Multimed. Tools Appl., vol. 76, no. 22, pp. 23353–23382
S. Teerakanok and T. Uehara.(2018). Copy-move forgery detection using GLCM-based rotation-invariant feature: A preliminary research. In Proc. COMPSAC,pp. 365–369
Chou, C.-L. and J.-C. Lee.(2018). Copy-Move Forgery Detection Based on Local Gabor Wavelets Patterns. In Proc. of Springer on International Conference on Security with Intelligent Computing and Big-data Services, vol. 733, pp 47-56
B. Chen, M. Yu, Q. Su, H. J. Shim and Y. Shi.(2018). Fractional Quaternion Zernike Moments for Robust Color Image Copy-Move Counterfeit Detection. in IEEE Access, vol. 6, pp. 56637-56646
M. Emam, Q. Han, Q. Li, and H. Zhang,(2017). A robust detection algorithm for image Copy-Move forgery in smooth regions. in Proc. ICCSS, London, U.K., Jul. 2017, pp. 119–123.
Thajeel, Salam & Shakir, Ali & Rasheed, Waleed & Sulong, Ghazali. (2019). Detection Copy-Move Forgery in Image Via Quaternion Polar Harmonic Transforms. KSII Transactions on Internet and Information Systems. 13. 4005-4025. 10.3837/tiis.2019.08.010.
Mahmood, T., et al. (2018). A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform. Journal of Visual Communication and Image Representation, vol. 53, pp. 202-214
Hosny, K.M., H.M. Hamza, and N.A. Lashin, "Copy-move forgery detection of duplicated objects using accurate PCET moments and morphological operators," The Imaging Science Journal, vol. 66, no. 6, pp. 330-345
Islam, M.M.; Karmakar, G.; Kamruzzaman, J.; Murshed, M. A .(2020).Robust Forgery Detection Method for Copy–Move and Splicing Attacks in Images. Electronics , 9, 1500.
R. Cristin, J. P. Ananth and V. Cyril Raj.(2018).Illumination-based texture descriptor and fruitfly support vector neural network for image counterfeit detection in face images. in IET Image Processing, vol. 12, no. 8, pp. 1439-1449
J. H. Bappy, C. Simons, L. Nataraj, B. S. Manjunath and A. K. Roy-Chowdhury.(2019). Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries. In IEEE Transactions on Image Processing, vol. 28, no. 7, pp. 3286-3300
Liu, Y., Q. Guan, and X. Zhao.(2018). Copy-move forgery detection based on convolutional kernel network. Multimedia Tools and Applications, vol. 77, no. 14, pp. 18269-18293
Al-Moadhen, Ahmed Abdulhadi Ahmed & Ridha, Mustafa & Khayeat, Ali.(2020). Splicing detection in color image based on deep learning of wavelet decomposed image. AIP Conference Proceedings. 2290. 10.1063/5.0027442.
Huang, HY., Ciou, AJ. Copy-move forgery detection for image forensics using the superpixel segmentation and the Helmert transformation. J Image Video Proc. 2019, 68 (2019).
Rodriguez-Ortega, Yohanna & Ballesteros, Dora & Renza, Diego. (2021). Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics. Journal of Imaging.7.59.10.3390/jimaging7030059.
Patil, D. N. N. . (2021). Liver Tissue Based Disease Detection Using Pre-Processing and Feature Extraction Techniques. Research Journal of Computer Systems and Engineering, 2(2), 17:21. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/27
Mr. Kunal Verma, Mr. Dharmesh Dhabliya. (2015). Design of Hand Motion Assist Robot for Rehabilitation Physiotherapy. International Journal of New Practices in Management and Engineering, 4(04), 07 - 11. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/40
Dhabliya, D. Security analysis of password schemes using virtual environment (2019) International Journal of Advanced Science and Technology, 28 (20), pp. 1334-1339.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
