Adaptive Threshold-based Reserved G2NN Feature Matching with Hybrid Deep Feature Learning for Copy-Move Image Forgery Detection
Keywords:
Copy-Move Image Forgery Detection; Hybrid Deep Feature Learning; Adaptive Threshold-based Reserved G2NN Feature Matching; Intermixed Forest and Cuckoo Search AlgorithmAbstract
Different image editing devices have been utilized for performing image forgery activities on social media in recent days. Then, the copied images are placed in various locations of the image. But, the important disadvantage of using these forgery detection approaches is detecting the tampered regions with less efficiency. The ultimate aim of this scheme is to investigate novel copy-move image forgery identification with the assistance of deep learning and matching procedure. In the first step, the benchmark datasets are gathered from different public sources and perform pre-processing using the Weiner filtering and contrast stretching process. Further, the feature extraction is done by a new hybrid deep feature learning method that integrates both the deep learning network called Enhanced Convolutional Neural Network (CNN) and Speeded Up Robust Features (SURF). With these hybrid features, feature matching is accomplished by the improved technique termed Adaptive Threshold-based Reserved Generalized Two Nearest Neighbourhood (G2NN) Feature Matching (AT-RG2NN-FM). The significant intention of the implemented scheme is to perform the optimal feature matching to attain the maximum detection rate. The performance of CNN and feature matching is enhanced by an Intermixed Forest and Cuckoo Search Algorithm (IFCSA). The experimental validation proves the effectiveness of the developed model.
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E. Ardizzone, A. Bruno, and G. Mazzola, “Copy–move forgery detection by matching triangles of key points,” IEEE Transactions on Information Forensics and Security, Vol.10, pp. 2084-2094, 2015.
A. Novozamsky, M. Sorel, “Detection of copy-move image modification using JPEG compression model”, Forensic Science International, Vol. 283, Pp. 47-57, 2018.
Y. Wu, W. Abd-Almageed, and P. Natarajan, “Image Copy-Move Forgery Detection via an End-to-End Deep Neural Network”, IEEE Winter Conference on Applications of Computer Vision, 2018.
J. Li, X. Li, B. Yang, and Xingming, “Segmentation-based Image Copy-move Forgery Detection Scheme”, IEEE Transactions on Information Forensics and Security, 2015.
I. Amerini, L. Ballan, R. Caldelli, A. D. Bimbo, L. D. Tongo, G. Serra, “Copy-move forgery detection and localization by means of robust clustering with J-Linkage”, Signal Processing: Image Communication, Vol. 28, 2013.
T. Mahmood, T. Nawaz, AunIrtaza, R. Ashraf, M. Shah, and M. Tariq Mahmood, “Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images”, Mathematical Problems in Engineering, 2016.
F. Yanga, J. Lia, W. Lua, J. Wengb, “Copy-move forgery detection based on hybrid features", Engineering Applications of Artificial Intelligence, 2017.
M. H. Alkawaz, G. Sulong, T. Saba and A. Rehman, "Detection of copy-move image forgery based on discrete cosine transform", Natural Computing Applications, 2016.
D. Cozzolino, G. Poggi and L. Verdoliv, “Efficient dense-field copy-move forgery detection,” IEEE Transactions on Information Forensics and Security, Vol. 10, no. 11, pp.2284-2297, 2015.
A. H. Oyiza, M. A. Maarof, “An improved DCT block-based technique for copy-move forgery detection in medical images”, International Journal of Innovative Computing, 2018.
C. –S. Park, J. Y. Choeh, "Fast and robust copy-move forgery detection based on scale-space representation", Multimedia Tools and Applications, Vol.77, 2018.
E. Silva, T. Carvalho, A. Ferreira, A. Rocha, “Going deeper into copy-move forgery detection: Exploring image telltales via multi-scale analysis and voting processes", Journal of Visual Communication and Image Representation, Vol.29, Pp16-32, 2015.
N. Kanagavalli and L. Latha, "A survey of copy-move image forgery detection techniques," International Conference on Inventive Systems and Control (ICISC), pp. 1-6,2017.
I. Amerini, L. Ballan, R. Caldelli, Alberto Del Bimbo and Giuseppe 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, 2011.
B. Yang, X. Sun, H. Guo, Z. Xia, X. Chen, "A copy-move forgery detection method based on CMFD-SIFT", Multimedia Tools and Applications, 2017.
Y. Huang, W. Lu, W. Sun, D. Long, “Improved DCT-based detection of copy-move forgery in images”, Forensic Science International, Vol.206, Pp.178-184, 2011.
O. M. Al-Qershi and B. E. Khoo, “Comparison of Matching Methods for Copy-Move Image Forgery Detection”, Signal Processing and power applications, 2017.
S. Dhivya, J. Sangeetha, B. Sudhakar, “Copy-move forgery detection using SURF feature extraction and SVMsupervised learning technique”, Soft Computing, Vol. 24, 2020.
B. Diallo, T. Urruty, P. Bourdon, C. Fernandez-Maloigne, ”Robust forgery detection for compressed images using CNN supervision”, Digital Forensics, 2020.
Abhishek, N. Jindal, “Copy move and splicing forgery detection using deep convolution neural network, and semantic segmentation”, Multimedia Tools and Applications,2020.
H. Chen, X. Yang, Y. Lyu, “Copy-Move Forgery Detection Based on Keypoint Clustering and Similar Neighborhood SearchAlgorithm”, Digital Object Identifier, 2020.
Y. Zhu, C. Chen, G. Yan, Y. Guo, and Y. Dong,” AR-Net: Adaptive Attention and ResidualRefinement Network for Copy-MoveForgery Detection”, IEEE Transactions on Industrial Informatics, 2020.
R. Agarwal, O. P. Verma, “An efficient copy move forgery detection using deep learning feature extraction and matching algorithm”, Multimedia Tools and Applications, 2019.
M. A. Elaskily, H. A. Elnemr, A. Sedik, M. M. Dessouky, G. M. ElBanby, O. A. Elshakankiry, A. A. M. Khalaf, H. K. Aslan, O. S. Faragallah, F. E. A. El-Samie, ”A novel deep learning framework for copy-move forgery detection in images”, Multimedia Tools and Applications, 2020.
B. Elhaminia, A. Harati, A. Taherinia, “A probabilistic framework for copy-move forgery detection based on Markov Random Field”, Multimedia Tools and Applications, 2019.
J. Cardoso M. Santos, G. A. Carrijo, C. F. S. Cardoso, J. C. Ferreira, P. M. Sousa and A. C. Patrocínio, "Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter," Research on Biomedical Engineering, vol. 36, pp. 107–119, 2020.
S. Firmansyah, and S. Anwar, "Perbaikan Citra Malam (Tidak Infrared) Dengan Metode Histogram Equalization Dan Contrast Stretching," Jurnal Ilmu Pengetahuan Dan Teknologi Komputer, Vol. 4. No. 2 February 2019.
M. Zahid, F. Ahmed, N. Javaid, R. A. Abbasi, H. S. Z. Kazmi, A. Javaid, M. Bilal, M. Akbar and M. Ilahi, "Electricity Prices and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids," Electronics, vol. 8, pp. 122, 2019.
S. Lu, X. Hu, C. Wang, L. Chen, S. Han, Y. Han, "Copy-move image forgery detection based on evolving circular domains coverage," Multimedia Tools and Applications, vol. 81, pp. 37847–37872, 2022.
M. Ghaemi, and Mohammad-RezaFeizi-Derakhshi, "Forest Optimization Algorithm", Expert Systems with Applications, vol.41, no.15, pp.6676-6687, November 2014.
A. S. Joshi, O. Kulkarni, G. M. Kakandikar, and V. M. Nandedkar, "Cuckoo Search Optimization- A Review", Materials Today: Proceedings, vol.4, no.8, pp.7262-7269, 2017.
A. G. Gad, "Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review," Archives of Computational Methods in Engineering, vol. 29, pp. 2531–2561, 2022.
M. Jain, V. Singh, A. Rani, "A novel nature-inspired algorithm for optimization: Squirrel search algorithm," Swarm and Evolutionary Computation, Vol. 44, pp. 148-175, February 2019.
F. Maher Al_azrak, Z. F. Elsharkawy, A. S. Elkorany, G. M. E. Banby, M. I. Dessowky & F. E. A. El-Samie, "Copy-Move Forgery Detection Based on Discrete and SURF Transforms," Wireless Personal Communications, vol. 110, pp. 503–530, 2020.
S. Lu, X. Hu, C. Wang, L. Chen, S. Han, Y. Han, "Copy-move image forgery detection based on evolving circular domains coverage," Multimedia Tools and Applications, vol. 81, pp. 37847–37872, 2022.
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