أسلوب كفوء لتحديد موقع تزوير Copy-Move باستخدام تقنية PointRend مع RegNetX

Kasban H, Nassar S. An efficient approach for forgery detection in digital images using Hilbert–Huang transform. Appl Soft Comput. 2020; 97: 106728. https://doi.org/10.1016/j.asoc.2020.106728

Ahmed B, Gulliver TA, alZahir S. Image splicing detection using mask-RCNN. Signal Image Video Process. 2020; 14(5): 1035-42. https://doi.org/10.1007/s11760-020-01636-0

Kadam KD, Ahirrao S, Kotecha K. Efficient approach towards detection and identification of copy move and image splicing forgeries using mask R-CNN with MobileNet V1. Comput Intell Neurosci. 2022; 2022. https://doi.org/10.1155/2022/6845326

Abdalla Y, Iqbal MT, Shehata M. Convolutional neural network for copy-move forgery detection. Symmetry. 2019; 11(10): 1280. https://doi.org/10.3390/sym11101280

Harba ES, Harba HS, Abdulmunem IA. Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks. Baghdad Sci J. 2021; 18(4):1317-1327. https://doi.org/10.21123/bsj.2021.18.4.1317

Khalaf M, Dhannoon BN. MSRD-Unet: Multiscale Residual Dilated U-Net for Medical Image Segmentation. Baghdad Sci J. 2022; 19(6 Supplement):1603-1611. https://doi.org/10.21123/bsj.2022.7559

Bondi L, Lameri S, Güera D, Bestagini P, Delp EJ, Tubaro S, editors. Tampering Detection and Localization Through Clustering of Camera-Based CNN Features. IEEE Conf Comp Vis Pattern Recognit Workshops. 2017. https://doi.org/10.1109/CVPRW.2017.232

Goel N, Kaur S, Bala R. Dual branch convolutional neural network for copy move forgery detection. IET Image Process. 2021: 656-65. https://doi.org/10.1049/ipr2.12051

Shi Z, Shen X, Kang H, Lv Y. Image manipulation detection and localization based on the dual-domain convolutional neural networks. IEEE Access. 2018: 76437-53. https://doi.org/10.1109/ACCESS.2018.2883588

Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM. 2017; 60(6): 84-90. https://doi.org/10.1145/3065386

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 14091556. 2014. https://doi.org/10.48550/arXiv.1409.1556

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al., editors. Going deeper with convolutions. Proc IEEE conf comp vis pattern recognit. 2015. https://doi.org/10.1109/CVPR.2015.7298594

He K, Zhang X, Ren S, Sun J, editors. Deep residual learning for image recognition. Proc IEEE conf comp vis pattern recognit. 2016. https://doi.org/10.1109/CVPR.2016.90

Muzaffer G, Ulutas G, editors. A new deep learning-based method to detection of copy-move forgery in digital images. 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT); 2019: IEEE. https://doi.org/10.1109/EBBT.2019.8741657

Samir S, Emary E, El-Sayed K, Onsi H. Optimization of a pre-trained AlexNet model for detecting and localizing image forgeries. Information. 2020: 275. https://doi.org/10.3390/info11050275

Agarwal R, Verma OP. An efficient copy move forgery detection using deep learning feature extraction and matching algorithm. Multimed Tools Appl.. 2019: 1-22. https://doi.org/10.1007/s11042-019-08495-z

Wu Y, Abd-Almageed W, Natarajan P, editors. Image copy-move forgery detection via an end-to-end deep neural network. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV); 2018: IEEE. https://doi.org/10.1109/WACV.2018.00211

Wang X, Wang H, Niu S, Zhang J. Detection and localization of image forgeries using improved mask regional convolutional neural network. Math Biosci Eng. 2019; 16(5): 4581-93. https://doi.org/10.3934/mbe.2019229

Ahmed B, Gulliver TA, alZahir S. Image splicing detection using mask-RCNN. Signal Image Video Process. 2020; 14:1035-42. https://doi.org/10.1007/s11760-020-01636-0

Kirillov A, Wu Y, He K, Girshick R, editors. Pointrend: Image segmentation as rendering. Proceedings of the IEEE/CVF conf comp r vis pattern recognit. 2020. https://doi.org/10.1109/CVPR42600.2020.00982

Shan B, Fang Y. A cross entropy based deep neural network model for road extraction from satellite images. Entropy. 2020; 22(5): 535. https://doi.org/10.3390/e22050535

Duan E, Wang L, Wang H, Hao H, Li R. Feed weight estimation model for health monitoring of meat rabbits based on deep learning. Int J Agric Biol Eng. 2022; 15(1): 233-40. https://doi.org/10.25165/j.ijabe.20221501.6797

Dong Y, Zhang Y, Hou Y, Tong X, Wu Q, Zhou Z, et al. Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction. Adv Civ Eng. 2022; 2022. https://doi.org/10.1155/2022/5995999

Li L, Zhang S, Wang B. Apple leaf disease identification with a small and imbalanced dataset based on lightweight convolutional networks. Sensors. 2021; 22(1): 173. https://doi.org/10.3390/s22010173

Zeng X, Wei S, Wei J, Zhou Z, Shi J, Zhang X, et al. CPISNet: delving into consistent proposals of instance segmentation network for high-resolution aerial images. Remote Sens. 2021; 13(14): 2788. https://doi.org/10.3390/rs13142788

Kadam K, Ahirrao S, Kotecha K, Sahu S. Detection and localization of multiple image splicing using MobileNet V1. IEEE Access. 2021; 9: 162499-519. https://doi.org/10.1109/ACCESS.2021.3130342

Peña Moliner D. Extending object classificatiion convolutional neural networks to custom logo detection: Universitat Politècnica de Catalunya; 2020. https://oa.upm.es/57088/

Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, et al. A Survey of Deep Learning-Based Object Detection. IEEE Access. 2019; 7:128837-68. https://doi.org/10.1109/ACCESS.2019.2939201

Amerini I, Ballan L, Caldelli R, Bimbo AD, Serra G. A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery. IEEE Transactions on Information Forensics and Security. 2011;6(3):1099-110. https://doi.org/10.1109/TIFS.2011.2129512