ARCHIVES

1. Farid, H. (2009). A survey of image forgery detection. IEEE Signal Processing Magazine, 26(2), 16-25.
https://doi.org/10.1109/MSP.2008.931079
2. Piva, A. (2013). An overview on image forensics. ISRN Signal Processing, 2013, Article 496701. https://doi.org/10.1155/2013/496701
3. Fridrich, J., Soukal, D., & Lukas, J. (2003). Detection of copy-move forgery in digital images. Proceedings of the Digital Forensic
Research Workshop (DFRWS), Cleveland, OH.
4. Durao, M. (2010). An approach to detect copy-paste image forgery using discrete wavelet transform. Proceedings of the IEEE
International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 588-593.
5. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91-
110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
6. Bay, H., Tuytelaars, T., & Van Gool, L. (2006). SURF: Speeded up robust features. In Proceedings of the European Conference on
Computer Vision (ECCV), Lecture Notes in Computer Science, vol. 3951, pp. 404-417.
7. Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. In Proceedings of the
International Conference on Computer Vision (ICCV), pp. 2564-2571.
8. Fridrich, J., Soukal, D., & Lukas, J. (2003). Detection of copy-move forgery in digital images. Digital Forensic Research Workshop
(DFRWS), Proceedings of the 2003 Digital Forensics Research Conference.
9. Popescu, A. C., & Farid, H. (2004). Exposing digital forgeries by detecting duplicated image regions. Technical Report TR2004-515,
Dartmouth College Department of Computer Science.
10. Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., & Serra, G. (2011). A SIFT-based forensic method for copy-move attack detection
and transformation recovery. IEEE Transactions on Information Forensics and Security, 6(3), 1099-1110.
https://doi.org/10.1109/TIFS.2011.2129512
11. Bo, X., Junwen, W., Guangjie, L., & Yuewei, D. (2010). Image copy-move forgery detection based on SURF. In Proceedings of the
IEEE International Conference on Multimedia Information Networking and Security (MINES), pp. 889-892.
12. Zhao, J., & Guo, J. (2013). Passive forensics for copy-move image forgery using a method based on DCT and SIFT. Forensic Science
International, 233(1-3), 158-166. https://doi.org/10.1016/j.forsciint.2013.09.013
13. Walia, S., & Kumar, K. (2019). Digital image forgery detection: A systematic scrutiny. Australian Journal of Forensic Sciences, 51(5),
488-526. https://doi.org/10.1080/00450618.2017.1373773
14. Wu, Y., AbdAlmageed, W., & Natarajan, P. (2019). ManTra-Net: Manipulation tracing network for detection and localization of image
forgeries with anomalous features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
pp. 9543-9552.
15. Dong, J., Wang, W., & Tan, T. (2013). CASIA image tampering detection evaluation database. In Proceedings of the IEEE China
Summit and International Conference on Signal and Information Processing (ChinaSIP), pp. 422-426.
16. Luo, W., Huang, J., & Qiu, G. (2010). Robust detection of region-duplication forgery in digital image. In Proceedings of the 18th
International Conference on Pattern Recognition (ICPR), Vol. 4, pp. 746-749.
17. Wenchao, C., & Jianhua, W. (2019). Image copy-move forgery detection based on deep learning. In Proceedings of the IEEE
International Conference on Industrial Informatics (INDIN), pp. 319-324. https://doi.org/10.1109/INDIN41052.2019.8972040.