Current - Issue
Year 2026 · Volume 5 · Issue 3
Original Article
Comparative Evolution of Digital Image Forgery Detection: From Residual-Saliency Processing to Variational Attention Learning
P. Muniasamy1
Dr. B. Srinivasan2
1 Assistant Professor, Department of Information Technology, Sri Ramakrishna Mission Vidyalaya College of Arts and Science (Autonomous), Coimbatore, Tamil Nadu, India. 2 Associate Professor, Department of Computer Science, Gobi Arts & Science College (Autonomous), Gobichettipalayam, Erode, Tamil Nadu, India.
Published Online: September-December 2026
Pages: 101-105
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260503013
References
1. Muniasamy P, & Srinivasan B. An Exhaustive Survey based on Image Forgery and Recompression Strategies. Indian Journal of Natural Sciences. 2026;16(94):107126-107132. ISSN: 0976-0997.
2. Muniasamy P, & Srinivasan B. (2026). Dynamic Residual Forgery-Aware Denoising and Gradient-Aware Patch Saliency for Robust Image Forgery Detection. Journal of Emerging Technologies and Innovative Research (JETIR), 13(7), b527–b537. https://doi.org/10.56975/jetir.v13i7.583977.
3. Bammey Q, von Gioi RG, Morel JM. Forgery detection by internal positional learning of demosaicing traces. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2022;328-338.
4. Zhu J, Li D, Fu X, Yang G, Huang J, Liu A, Zha JJ. Learning discriminative noise guidance for image forgery detection and localization. Proceedings of the AAAI Conference on Artificial Intelligence. 2024;38(7):7739-7747.
5. Gardella M, Musé P, Morel JM, Colom M. Forgery detection in digital images by multi-scale noise estimation. Journal of Imaging. 2021;7(7):119.
6. Ali SS, Ganapathi II, Vu NS, Ali SD, Saxena N, Werghi N. Image forgery detection using deep learning by recompressing images. Electronics. 2022;11(3):403.
7. Khalil AH, Ghalwash AZ, Elsayed HAG, Salama GI, Ghalwash HA. Enhancing digital image forgery detection using transfer learning. IEEE Access. 2023;11:91583-91594.
8. Zhang H, Zeng J, Yang J. Multi-Scale Edge-Guided Image Forgery Detection via Improved Self-Supervision and Self-Adversarial Training.
Electronics. 2025;14(9):1877.
9. Thakur A, Jindal N. Machine learning based saliency algorithm for image forgery classification and localization. Proceedings of the 2018 First International Conference on Secure Cyber Computing and Communication. 2018;451-456.
10. Anwar S, Barnes N. Real image denoising with feature attention. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019;3155-3164.
11. Elaskily MA, Alkinani MH, Sedik A, Dessouky MM. Deep learning based algorithm (ConvLSTM) for copy move forgery detection. Journal of Intelligent & Fuzzy Systems. 2021;40(3):4385-4405.
12. Maashi M, Alamro H, Mohsen H, Negm N, Mohammed GP, Ahmed NA, Ibrahim SS, Alsaid MI. Modeling of reptile search algorithm with deep learning approach for copy move image forgery detection. IEEE Access. 2023;11:87297-87304.
13. Tyagi S, Yadav D. ForensicNet: Modern convolutional neural network-based image forgery detection network. Journal of Forensic Sciences. 2023;68(2):461-469.
14. Mareen H, De Neve L, Lambert P, Van Wallendael G. Harmonizing Image Forgery Detection & Localization: Fusion of Complementary Approaches. Journal of Imaging. 2024;10(1):4.
15. Choubineh A, Chen J, Coenen F, Ma F. Applying Monte Carlo Dropout to quantify the uncertainty of skip connection-based convolutional neural networks optimized by big data. Electronics. 2023;12(6):1453.
16. Hosny KM, Mortda AM, Lashin NA, Fouda MM. A new method to detect splicing image forgery using convolutional neural network. Applied Sciences. 2023;13(3):1272.
17. Bevinamarad P, Unki P, Nidagundi P. Copy-move forgery detection and localization framework for images using stationary wavelet transform and hybrid dilated adaptive VGG16 with optimization strategy. International Journal of Image, Graphics and Signal Processing. 2024;16(1):38-60.
18. Nagm AM, Moussa MM, Shoitan R, Ali A, Mashhour M, Salama AS, AbdulWakel HI. Detecting image manipulation with ELA-CNN integration: a powerful framework for authenticity verification. PeerJ Computer Science. 2024;10:e2205.
19. Waseem S, Abu-Bakar SARS, Omar Z, Ahmad K, Bennamoun M. Multi-attention-based approach for deepfake face and expression swap detection and localization. Journal of Image and Video Processing. 2023;2023:14.
20. Patel Y, Raza SA, Cheema AA, Khan MS, Habib U, Usman M. An improved dense CNN architecture for deepfake image detection. IEEE Access. 2023;11:22081-22095.
21. Naskar G, Mohiuddin S, Malakar S, Cuevas E, Sarkar R. Deepfake detection using deep feature stacking and meta-learning. Heliyon. 2024;10(4):e25933.
22. Liu Y, Li X, Zhang J, Li S, Hu S, Lei J. Hierarchical Progressive Image Forgery Detection and Localization Method Based on UNet. Big Data and Cognitive Computing. 2024;8(9):119.
23. Zhang J, Huang X, Liu Y, Han Y, Xiang Z. GAN-based medical image small region forgery detection via a two-stage cascade framework. PLOS ONE. 2024;19(1):e0290303.
24. Dai G, Chen K, Huang L, Chen L, An D, Wang Z, Wang K. CECL-Net: Contrastive Learning and Edge-Reconstruction-Driven Complementary Learning Network for Image Forgery Localization. Electronics. 2024;13(19):3919.
25. Lin X, Tang W, Wang H, Liu Y, Ju Y, Wang S, Yu Z. Exposing image splicing traces in scientific publications via uncertainty-guided refinement. Patterns. 2024;5(9):101038.
2. Muniasamy P, & Srinivasan B. (2026). Dynamic Residual Forgery-Aware Denoising and Gradient-Aware Patch Saliency for Robust Image Forgery Detection. Journal of Emerging Technologies and Innovative Research (JETIR), 13(7), b527–b537. https://doi.org/10.56975/jetir.v13i7.583977.
3. Bammey Q, von Gioi RG, Morel JM. Forgery detection by internal positional learning of demosaicing traces. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2022;328-338.
4. Zhu J, Li D, Fu X, Yang G, Huang J, Liu A, Zha JJ. Learning discriminative noise guidance for image forgery detection and localization. Proceedings of the AAAI Conference on Artificial Intelligence. 2024;38(7):7739-7747.
5. Gardella M, Musé P, Morel JM, Colom M. Forgery detection in digital images by multi-scale noise estimation. Journal of Imaging. 2021;7(7):119.
6. Ali SS, Ganapathi II, Vu NS, Ali SD, Saxena N, Werghi N. Image forgery detection using deep learning by recompressing images. Electronics. 2022;11(3):403.
7. Khalil AH, Ghalwash AZ, Elsayed HAG, Salama GI, Ghalwash HA. Enhancing digital image forgery detection using transfer learning. IEEE Access. 2023;11:91583-91594.
8. Zhang H, Zeng J, Yang J. Multi-Scale Edge-Guided Image Forgery Detection via Improved Self-Supervision and Self-Adversarial Training.
Electronics. 2025;14(9):1877.
9. Thakur A, Jindal N. Machine learning based saliency algorithm for image forgery classification and localization. Proceedings of the 2018 First International Conference on Secure Cyber Computing and Communication. 2018;451-456.
10. Anwar S, Barnes N. Real image denoising with feature attention. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019;3155-3164.
11. Elaskily MA, Alkinani MH, Sedik A, Dessouky MM. Deep learning based algorithm (ConvLSTM) for copy move forgery detection. Journal of Intelligent & Fuzzy Systems. 2021;40(3):4385-4405.
12. Maashi M, Alamro H, Mohsen H, Negm N, Mohammed GP, Ahmed NA, Ibrahim SS, Alsaid MI. Modeling of reptile search algorithm with deep learning approach for copy move image forgery detection. IEEE Access. 2023;11:87297-87304.
13. Tyagi S, Yadav D. ForensicNet: Modern convolutional neural network-based image forgery detection network. Journal of Forensic Sciences. 2023;68(2):461-469.
14. Mareen H, De Neve L, Lambert P, Van Wallendael G. Harmonizing Image Forgery Detection & Localization: Fusion of Complementary Approaches. Journal of Imaging. 2024;10(1):4.
15. Choubineh A, Chen J, Coenen F, Ma F. Applying Monte Carlo Dropout to quantify the uncertainty of skip connection-based convolutional neural networks optimized by big data. Electronics. 2023;12(6):1453.
16. Hosny KM, Mortda AM, Lashin NA, Fouda MM. A new method to detect splicing image forgery using convolutional neural network. Applied Sciences. 2023;13(3):1272.
17. Bevinamarad P, Unki P, Nidagundi P. Copy-move forgery detection and localization framework for images using stationary wavelet transform and hybrid dilated adaptive VGG16 with optimization strategy. International Journal of Image, Graphics and Signal Processing. 2024;16(1):38-60.
18. Nagm AM, Moussa MM, Shoitan R, Ali A, Mashhour M, Salama AS, AbdulWakel HI. Detecting image manipulation with ELA-CNN integration: a powerful framework for authenticity verification. PeerJ Computer Science. 2024;10:e2205.
19. Waseem S, Abu-Bakar SARS, Omar Z, Ahmad K, Bennamoun M. Multi-attention-based approach for deepfake face and expression swap detection and localization. Journal of Image and Video Processing. 2023;2023:14.
20. Patel Y, Raza SA, Cheema AA, Khan MS, Habib U, Usman M. An improved dense CNN architecture for deepfake image detection. IEEE Access. 2023;11:22081-22095.
21. Naskar G, Mohiuddin S, Malakar S, Cuevas E, Sarkar R. Deepfake detection using deep feature stacking and meta-learning. Heliyon. 2024;10(4):e25933.
22. Liu Y, Li X, Zhang J, Li S, Hu S, Lei J. Hierarchical Progressive Image Forgery Detection and Localization Method Based on UNet. Big Data and Cognitive Computing. 2024;8(9):119.
23. Zhang J, Huang X, Liu Y, Han Y, Xiang Z. GAN-based medical image small region forgery detection via a two-stage cascade framework. PLOS ONE. 2024;19(1):e0290303.
24. Dai G, Chen K, Huang L, Chen L, An D, Wang Z, Wang K. CECL-Net: Contrastive Learning and Edge-Reconstruction-Driven Complementary Learning Network for Image Forgery Localization. Electronics. 2024;13(19):3919.
25. Lin X, Tang W, Wang H, Liu Y, Ju Y, Wang S, Yu Z. Exposing image splicing traces in scientific publications via uncertainty-guided refinement. Patterns. 2024;5(9):101038.
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