Efficient Approach for the Localization of Copy-Move Forgeries Using PointRend with RegNetX
Main Article Content
Abstract
Digital images are one of the modern era's dominant sources of information and communication. However, an image can simply be altered with the existence of several tools for image editing. These altered images can transmit across platforms of social media to influence some people in society and may have both positive and negative effects. Therefore, the development of technology becomes a necessary issue to detect and localize a forgery in an image. Copy-move forgery (CMF) is one of the most popular forgeries. In CMF, the new forgery image is created by copying a part presented in an image and placing it at a different location on the same image. This paper proposes a PointRend as a technique to localize copy-move forgeries. This work also presents the PointRend framework with a lightweight backbone model RegNetX (PointRend-RegNetX) to detect such forgeries. From the comparative analysis of the proposed technique with ResNet-50 backbone on two standard datasets, it has been shown that the proposed model (PointRend-RegNetX) is superior in MICC F-220 and MICC F-2000 datasets for images that contain copy-move forgeries. In instances segmentation of forged regions, the improved model (PointRend-RegNetX) has achieved a mean average precision (mAP) of 88.5% on MICC F-220 dataset and 86.4 % on MICC F-2000 dataset.
Received 02/01/2023
Revised 23/04/2023
Accepted 25/04/2023
Published Online First 20/09/2023
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
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