1. bookVolumen 22 (2022): Edición 4 (November 2022)
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Formato
Revista
eISSN
1314-4081
Primera edición
13 Mar 2012
Calendario de la edición
4 veces al año
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Acceso abierto

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

Publicado en línea: 10 Nov 2022
Volumen & Edición: Volumen 22 (2022) - Edición 4 (November 2022)
Páginas: 91 - 110
Recibido: 31 Jan 2022
Aceptado: 25 Aug 2022
Detalles de la revista
License
Formato
Revista
eISSN
1314-4081
Primera edición
13 Mar 2012
Calendario de la edición
4 veces al año
Idiomas
Inglés

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

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

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. Abierto DOISearch in Google Scholar

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 Abierto DOISearch in Google Scholar

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

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

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

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. Abierto DOISearch in Google Scholar

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

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

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. Abierto DOISearch in Google Scholar

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

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

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

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

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

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

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

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

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. Abierto DOISearch in Google Scholar

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

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

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

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

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

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

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. Abierto DOISearch in Google Scholar

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

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. Abierto DOISearch in Google Scholar

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. Abierto DOISearch in Google Scholar

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

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

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

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

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. Abierto DOISearch in Google Scholar

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. Abierto DOISearch in Google Scholar

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. Abierto DOISearch in Google Scholar

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. Abierto DOISearch in Google Scholar

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

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

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. Abierto DOISearch in Google Scholar

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