Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model

Tinnathi, Sreenivasu; Sudhavani, G.

doi:10.2478/cait-2022-0041

References

1. Wang, X. Y., C. Wang, L. Wang, L. X. Jiao, H. Y. Yang, P. P. Niu. A Fast and High Accurate Image Copy-Move Forgery Detection Approach. – Multidimensional Systems and Signal Processing, Vol. 31, 2020, pp. 857-883. https://doi.org/10.1007/s11045-019-00688-x10.1007/s11045-019-00688-x
Search in Google Scholar Back to article
2. Mahmood, T., Z. Mehmood, M. Shah, T. Saba. 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, 2018, pp. 202-214. https://doi.org/10.1016/j.jvcir.2018.03.01510.1016/j.jvcir.2018.03.015
Search in Google Scholar Back to article
3. Wu, Y., W. Abd-Almageed, P. Natarajan. Image Copy-Move Forgery Detection via an End-to-End Deep Neural Network. – In: Proc. of IEEE Winter Conference on Applications of Computer Vision (WACV’18), IEEE, 12-15 March 2018, Lake Tahoe, NV, USA, pp. 1907-1915. DOI: 10.1109/WACV.2018.00211.
Open DOI Search in Google Scholar Back to article
4. Mahmood, T., A. Irtaza, Z. Mehmood, M. T. Mahmood. Copy-Move Forgery Detection through Stationary Wavelets and Local Binary Pattern Variance for Forensic Analysis in Digital Images. – Forensic Science International, Vol. 279, 2017, pp. 8-21. DOI: 10.1016/j.forsciint.2017.07.037.28841507
Open DOI Search in Google Scholar Back to article
5. Jin, G., X. Wan. An Improved Method for SIFT-Based Copy-Move Forgery Detection Using Non-Maximum Value Suppression and Optimized J-Linkage. – Signal Processing: Image Communication, Vol. 57, 2017, pp. 113-125. https://doi.org/10.1016/j.image.2017.05.01010.1016/j.image.2017.05.010
Search in Google Scholar Back to article
6. Bi, X., C. M. Pun. Fast Reflective Offset-Guided Searching Method for Copy-Move Forgery Detection. – Information Sciences, Vol. 418-419, 2017, pp. 531-545. https://doi.org/10.1016/j.ins.2017.08.04410.1016/j.ins.2017.08.044
Search in Google Scholar Back to article
7. Zhong, J. L., C. M. Pun, Y. F. Gan. Dense Moment Feature Index and Best Match Algorithms for Video Copy-Move Forgery Detection. – Information Sciences, Vol. 537, 2020, pp. 184-202. https://doi.org/10.1016/j.ins.2020.05.13410.1016/j.ins.2020.05.134
Search in Google Scholar Back to article
8. Islam, A., C. Long, A. Basharat, A. Hoogs. DOA-GAN: Dual-Order Attentive Generative Adversarial Network for Image Copy-Move Forgery Detection and Localization. – In: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 2020, pp. 4675-4684. DOI: 10.1109/CVPR42600.2020.00473.
Open DOI Search in Google Scholar Back to article
9. Yang, B., X. Sun, H. Guo, Z. Xia, X. Chen. A Copy-Move Forgery Detection Method Based on CMFD-SIFT. – Multimedia Tools and Applications, Vol. 77, 2019, pp. 837-855. https://doi.org/10.1007/s11042-016-4289-y10.1007/s11042-016-4289-y
Search in Google Scholar Back to article
10. Hosny, K. M., H. M. Hamza, N. A. Lashin. Copy-Move Forgery Detection of Duplicated Objects Using Accurate PCET Moments and Morphological Operators. – The Imaging Science Journal, Vol. 66, 2018, pp. 330-345. https://doi.org/10.1080/13682199.2018.146134510.1080/13682199.2018.1461345
Search in Google Scholar Back to article
11. Dixit, R., R. Naskar, S. Mishra. Blur-Invariant Copy-Move Forgery Detection Technique with Improved Detection Accuracy Utilizing SWT-SVD. – IET Image Processing, Vol. 11, 2011, pp. 301-309. DOI: 10.1049/iet-ipr.2016.0537.
Open DOI Search in Google Scholar Back to article
12. Wang, C., Z. Zhang, X. Zhou. An Image Copy-Move Forgery Detection Scheme Based on A-KAZE and SURF Features. – Symmetry, Vol. 10, 2018, pp. 706. https://doi.org/10.3390/sym1012070610.3390/sym10120706
Search in Google Scholar Back to article
13. Al-Qershi, O. M., B. E. Khoo. Enhanced Block-Based Copy-Move Forgery Detection Using k-Means Clustering. – Multidimensional Systems and Signal Processing, Vol. 30, 2019, pp. 1671-1695. https://doi.org/10.1007/s11045-018-0624-y10.1007/s11045-018-0624-y
Search in Google Scholar Back to article
14. Abdalla, Y., M. T. Iqbal, M. Shehata. Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network. – Information, Vol. 10, 2019, pp. 286. https://doi.org/10.3390/info1009028610.3390/info10090286
Search in Google Scholar Back to article
15. Tinnathi, S., 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, 2020, 102966. https://doi.org/10.1016/j.jvcir.2020.10296610.1016/j.jvcir.2020.102966
Search in Google Scholar Back to article
16. Kasban, H., S. Nassar. An Efficient Approach for Forgery Detection in Digital Images Using Hilbert-Huang Transform. – Applied Soft Computing, Vol. 97, pp. 106728. https://doi.org/10.1016/j.asoc.2020.10672810.1016/j.asoc.2020.106728
Search in Google Scholar Back to article
17. Elaskily, M. A., H. A. Elnemr, A. Sedik, M. M. Dessouky, G. M. El Banby, 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, Vol. 79, 2020, pp. 19167-19192. https://doi.org/10.1007/s11042-020-08751-710.1007/s11042-020-08751-7
Search in Google Scholar Back to article
18. Meena, K. B., V. Tyagi. A Copy-Move Image Forgery Detection Technique Based on Tetrolet Transform. – Journal of Information Security and Applications, Vol. 52, 2020, pp. 102481. https://doi.org/10.1016/j.jisa.2020.10248110.1016/j.jisa.2020.102481
Search in Google Scholar Back to article
19. Agarwal, R., O. P. Verma. An Efficient Copy Move Forgery Detection Using Deep Learning Feature Extraction and Matching Algorithm. – Multimedia Tools and Applications, Vol. 79, 2019, pp. 7355-7376. https://doi.org/10.1007/s11042-019-08495-z10.1007/s11042-019-08495-z
Search in Google Scholar Back to article
20. Zhu, Y., C. Chen, G. Yan, Y. Guo, Y. Dong. AR-Net: Adaptive Attention and Residual Refinement Network for Copy-Move Forgery Detection. – IEEE Transactions on Industrial Informatics, Vol. 16, 2020, pp. 6714-6723. DOI: 10.1109/TII.2020.2982705.
Open DOI Search in Google Scholar Back to article
21. Liu, Y., Q. Guan, X. Zhao. Copy-Move Forgery Detection Based on Convolutional Kernel Network. – Multimedia Tools and Applications, Vol. 77, 2018, pp. 18269-18293. https://doi.org/10.1007/s11042-017-5374-610.1007/s11042-017-5374-6
Search in Google Scholar Back to article
22. Lin, C., W. Lu, X. Huang, K. Liu, W. Sun, H. Lin, Z. Tan. Copy-Move Forgery Detection Using Combined Features and Transitive Matching. – Multimedia Tools and Applications, Vol. 78, 2018, pp. 30081-30096. https://doi.org/10.1007/s11042-018-6922-410.1007/s11042-018-6922-4
Search in Google Scholar Back to article
23. Alberry, H. A., A. A. Hegazy, G. I. Salama. A Fast SIFT Based Method for Copy Move Forgery Detection. – Future Computing and Informatics Journal, Vol. 3, 2018, pp. 159-165. https://doi.org/10.1016/j.fcij.2018.03.00110.1016/j.fcij.2018.03.001
Search in Google Scholar Back to article
24. Yang, F., J. Li, W. Lu, J. Weng. Copy-Move Forgery Detection Based on Hybrid Features. – Engineering Applications of Artificial Intelligence, Vol. 59, 2017, pp. 73-83. https://doi.org/10.1016/j.engappai.2016.12.02210.1016/j.engappai.2016.12.022
Search in Google Scholar Back to article
25. Niyishaka, P., C. Bhagvati. Copy-Move Forgery Detection Using Image Blobs and BRISK Feature. – Multimedia Tools and Applications, Vol. 79, 2020, pp. 26045-26059. https://doi.org/10.1007/s11042-020-09225-610.1007/s11042-020-09225-6
Search in Google Scholar Back to article
26. Huang, H. Y., A. J. Ciou. Copy-Move Forgery Detection for Image Forensics Using the Superpixel Segmentation and the Helmert Transformation. – EURASIP Journal on Image and Video Processing, 2019, pp. 689. https://doi.org/10.1186/s13640-019-0469-910.1186/s13640-019-0469-9
Search in Google Scholar Back to article
27. Wang, C., Z. Zhang, Q. Li, X. Zhou. An Image Copy-Move Forgery Detection Method Based on SURF and PCET. – IEEE Access, Vol. 7, 2019, pp. 170032-170047. DOI: 10.1109/ACCESS.2019.2955308.
Open DOI Search in Google Scholar Back to article
28. Raju, P. M., M. S. Nair. Copy-Move Forgery Detection Using Binary Discriminant Features. – Journal of King Saud University-Computer and Information Sciences, 2018. https://doi.org/10.1016/j.jksuci.2018.11.00410.1016/j.jksuci.2018.11.004
Search in Google Scholar Back to article
29. Gani, G., F. Qadir. A Robust Copy-Move Forgery Detection Technique Based on Discrete Cosine Transform and Cellular Automata. – Journal of Information Security and Applications, Vol. 54, 2020, pp. 102510. DOI: 10.1016/j.jisa.2020.102510.
Open DOI Search in Google Scholar Back to article
30. Soni, B. P. K., Das, D. M. Thounaojam. Geometric Transformation Invariant Block Based Copy-Move Forgery Detection Using Fast and Efficient Hybrid Local Features. – Journal of Information Security and Applications, Vol. 45, 2019, pp. 44-51. DOI: 10.1016/j.jisa.2019.01.007.
Open DOI Search in Google Scholar Back to article
31. Chen, C. C., W. Y. Lu, C. H. Chou. Rotational Copy-Move Forgery Detection Using SIFT and Region Growing Strategies. – Multimedia Tools and Applications, Vol. 78, 2019, pp. 18293-18308. https://doi.org/10.1007/s11042-019-7165-810.1007/s11042-019-7165-8
Search in Google Scholar Back to article
32. Park, J. Y., T. A. Kang, Y. H. Moon, I. K. Eom. Copy-Move Forgery Detection Using Scale Invariant Feature and Reduced Local Binary Pattern Histogram. – Symmetry, Vol. 12, 2020, pp. 492. https://doi.org/10.3390/sym1204049210.3390/sym12040492
Search in Google Scholar Back to article
33. Elhaminia, B., A. Harati, A. Taherinia. A Probabilistic Framework for Copy-Move Forgery Detection Based on Markov Random Field. – Multimedia Tools and Applications, Vol. 78, (2019), pp. 25591-25609. https://doi.org/10.1007/s11042-019-7713-210.1007/s11042-019-7713-2
Search in Google Scholar Back to article
34. Bilal, M., H. A. Habib, Z. Mehmood, R. M. Yousaf, T. Saba, A. Rehman. A Robust Technique for Copy-Move Forgery Detection from Small and Extremely Smooth Tampered Regions Based on the DHE-SURF Features and mDBSCAN Clustering. – Australian Journal of Forensic Sciences, Vol. 53, 2021, pp. 459-482. https://doi.org/10.1080/00450618.2020.171547910.1080/00450618.2020.1715479
Search in Google Scholar Back to article
35. Chen, B., M. Yu, Q. Su, H. J. Shim, Y. Q. Shi. Fractional Quaternion Zernike Moments for Robust Color Image Copy-Move Forgery Detection. – IEEE Access, Vol. 6, 2018, pp. 56637-56646. DOI: 10.1109/ACCESS.2018.2871952.
Open DOI Search in Google Scholar Back to article
36. Cozzolino, D., G. Poggi, L. Verdoliva. Efficient Dense-Field Copy-Move Forgery Detection. – IEEE Transactions on Information Forensics and Security, Vol. 10, 2015, pp. 2284-2297. DOI: 10.1109/TIFS.2015.2455334.
Open DOI Search in Google Scholar Back to article
37. Amerini, I., L. Ballan, R. Caldelli, A. D. Bimbo, G. Serra. A Sift-Based Forensic Method for Copy-Move Attack Detection and Transformation Recovery. – IEEE Transactions on Information Forensics and Security, Vol. 6, 2011, pp. 1099-1110. DOI: 10.1109/TIFS.2011.2129512.
Open DOI Search in Google Scholar Back to article
38. Ma, J., X. Wang, B. Xiao. An Image Segmentation Method Based on Simple Linear Iterative Clustering and Graph-Based Semi-Supervised Learning. – In: Proc. of International Conference on Orange Technologies (ICOT’15), IEEE, Hong Kong, China, 2015, pp. 10-13. DOI: 10.1109/ICOT.2015.7498477.
Open DOI Search in Google Scholar Back to article
39. Hegde, R. B., K. Prasad, H. Hebbar, B. M. K. Singh. Feature Extraction Using Traditional Image Processing and Convolutional Neural Network Methods to Classify White Blood Cells: A Study. – Australasian Physical & Engineering Sciences in Medicine, Vol. 42, 2017, pp. 627-638. https://doi.org/10.1007/s13246-019-00742-910.1007/s13246-019-00742-930830652
Search in Google Scholar Back to article
40. Goel, T., R. Murugan, S. Mirjalili, D. K. Chakrabartty. OptCoNet: An Optimized Convolutional Neural Network for an Automatic Diagnosis of COVID-19. – Applied Intelligence, Vol. 51, 2021, pp. 1351-1366. https://doi.org/10.1007/s10489-020-01904-z10.1007/s10489-020-01904-z750230834764551
Search in Google Scholar Back to article
41. Wu, C., J. Wang, X. Chen, P. Du, W. Yang. A Novel Hybrid System Based on Multi-Objective Optimization for Wind Speed Forecasting. – Renewable Energy, Vol. 146, 2020, pp. 149-165. DOI: 10.1016/j.renene.2019.04.157.
Open DOI Search in Google Scholar Back to article

Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model

References

Paradigm

My account