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AI-Based Image Forgery Detection System Using EfficientNet-B0 and Grad-CAM Explanations with a Web-Based Interface
Authors
1 2 3 4 Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India. 5 Professor, Project Mentor, Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India.
Article Information
- Published In
- Volume 6, Issue 6 (2026)
- Published Online
- November-December 2026
- Pages
- 62-68
- Article Type
- Original Article
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References
1. H. Farid, “Image forgery detection,” IEEE Signal Process. Mag., vol. 26, no. 2, pp. 16–25, 2009.
2. L. Verdoliva, “Media forensics and DeepFakes: An overview,” IEEE J. Sel. Topics Signal Process., vol. 14, no. 5, pp. 910–932, 2020, doi: 10.1109/JSTSP.2020.3002101.
3. J. Fridrich, D. Soukal, and J. Lukáš, “Detection of copy-move forgery in digital images,” in Proc. Digital Forensic Research Workshop, Cleveland, OH, USA, Aug. 2003.
4. B. Bayar and M. C. Stamm, “A deep learning approach to universal image manipulation detection using a new convolutional layer,” in Proc. 4th ACM Workshop Inf. Hiding Multimedia Security (IH&MMSec), 2016, doi: 10.1145/2909827.2930786.
5. P. Zhou, X. Han, V. I. Morariu, and L. S. Davis, “Learning rich features for image manipulation detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2018, pp. 1053–1061.
6. M. Zanardelli, F. Guerrini, R. Leonardi, and N. Adami, “Image forgery detection: A survey of recent deep-learning approaches,” Multimedia Tools Appl., vol. 82, pp. 17521–17566, 2023, doi: 10.1007/s11042-022-13797-w.
7. R. Tolosana, R. Vera-Rodriguez, J. Fierrez, A. Morales, and J. Ortega-Garcia, “DeepFakes and beyond: A survey of face manipulation and fake detection,” Inf. Fusion, vol. 64, pp. 131–148, 2020, arXiv:2001.00179.
8. D. Cozzolino, G. Poggi, and L. Verdoliva, “Splicebuster: A new blind image splicing detector,” in Proc. IEEE Int. Workshop Inf. Forensics Security (WIFS), 2015, pp. 1–6, doi: 10.1109/WIFS.2015.7368565.
9. B. Bayar and M. C. Stamm, “Constrained convolutional neural networks: A new approach towards general purpose image manipulation detection,” IEEE Trans. Inf. Forensics Security, vol. 13, no. 11, pp. 2691–2706, Nov. 2018, doi: 10.1109/TIFS.2018.2825953.
10. Y. Wu, W. AbdAlmageed, and P. Natarajan, “ManTra-Net: Manipulation tracing network for detection and localization of image forgeries with anomalous features,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2019, pp. 9543–9552.
11. A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, “FaceForensics++: Learning to detect manipulated facial images,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), 2019, pp. 1–11, doi: 10.1109/ICCV.2019.00009.
12. R. Caldelli, L. Galteri, I. Amerini, and A. Del Bimbo, “Optical flow based CNN for detection of unlearnt deepfake manipulations,” Pattern Recognit. Lett., vol. 146, pp. 31–37, 2021.
13. M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc. 36th Int. Conf. Mach. Learn. (ICML), ser. PMLR, vol. 97, 2019, pp. 6105–6114.
14. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” Int. J. Comput. Vis., vol. 128, no. 2, pp. 336–359, 2020, doi: 10.1007/s11263-019-01228-7.
15. J. Dong, W. Wang, and T. Tan, “CASIA image tampering detection evaluation database,” in Proc. IEEE China Summit Int. Conf. Signal Inf. Process. (ChinaSIP), Beijing, China, 2013, pp. 422–426, doi: 10.1109/ChinaSIP.2013.6625374.
16. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2009, pp. 248–255, doi: 10.1109/CVPR.2009.5206848.
17. M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” in Proc. 12th USENIX Symp. Operating Syst. Design Implementation (OSDI), 2016, pp. 265–283.
2. L. Verdoliva, “Media forensics and DeepFakes: An overview,” IEEE J. Sel. Topics Signal Process., vol. 14, no. 5, pp. 910–932, 2020, doi: 10.1109/JSTSP.2020.3002101.
3. J. Fridrich, D. Soukal, and J. Lukáš, “Detection of copy-move forgery in digital images,” in Proc. Digital Forensic Research Workshop, Cleveland, OH, USA, Aug. 2003.
4. B. Bayar and M. C. Stamm, “A deep learning approach to universal image manipulation detection using a new convolutional layer,” in Proc. 4th ACM Workshop Inf. Hiding Multimedia Security (IH&MMSec), 2016, doi: 10.1145/2909827.2930786.
5. P. Zhou, X. Han, V. I. Morariu, and L. S. Davis, “Learning rich features for image manipulation detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2018, pp. 1053–1061.
6. M. Zanardelli, F. Guerrini, R. Leonardi, and N. Adami, “Image forgery detection: A survey of recent deep-learning approaches,” Multimedia Tools Appl., vol. 82, pp. 17521–17566, 2023, doi: 10.1007/s11042-022-13797-w.
7. R. Tolosana, R. Vera-Rodriguez, J. Fierrez, A. Morales, and J. Ortega-Garcia, “DeepFakes and beyond: A survey of face manipulation and fake detection,” Inf. Fusion, vol. 64, pp. 131–148, 2020, arXiv:2001.00179.
8. D. Cozzolino, G. Poggi, and L. Verdoliva, “Splicebuster: A new blind image splicing detector,” in Proc. IEEE Int. Workshop Inf. Forensics Security (WIFS), 2015, pp. 1–6, doi: 10.1109/WIFS.2015.7368565.
9. B. Bayar and M. C. Stamm, “Constrained convolutional neural networks: A new approach towards general purpose image manipulation detection,” IEEE Trans. Inf. Forensics Security, vol. 13, no. 11, pp. 2691–2706, Nov. 2018, doi: 10.1109/TIFS.2018.2825953.
10. Y. Wu, W. AbdAlmageed, and P. Natarajan, “ManTra-Net: Manipulation tracing network for detection and localization of image forgeries with anomalous features,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2019, pp. 9543–9552.
11. A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, “FaceForensics++: Learning to detect manipulated facial images,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), 2019, pp. 1–11, doi: 10.1109/ICCV.2019.00009.
12. R. Caldelli, L. Galteri, I. Amerini, and A. Del Bimbo, “Optical flow based CNN for detection of unlearnt deepfake manipulations,” Pattern Recognit. Lett., vol. 146, pp. 31–37, 2021.
13. M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc. 36th Int. Conf. Mach. Learn. (ICML), ser. PMLR, vol. 97, 2019, pp. 6105–6114.
14. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” Int. J. Comput. Vis., vol. 128, no. 2, pp. 336–359, 2020, doi: 10.1007/s11263-019-01228-7.
15. J. Dong, W. Wang, and T. Tan, “CASIA image tampering detection evaluation database,” in Proc. IEEE China Summit Int. Conf. Signal Inf. Process. (ChinaSIP), Beijing, China, 2013, pp. 422–426, doi: 10.1109/ChinaSIP.2013.6625374.
16. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2009, pp. 248–255, doi: 10.1109/CVPR.2009.5206848.
17. M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” in Proc. 12th USENIX Symp. Operating Syst. Design Implementation (OSDI), 2016, pp. 265–283.
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