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CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images
Authors
1 Professor, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India. 2 3 4 5 Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India.
Article Information
- Published In
- Volume 5, Issue 6 (2025)
- Published Online
- November-December 2025
- Pages
- 227-230
- Article Type
- Original Article
- Check for updates
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References
1. J. J. Bird and A. Lotfi, "CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images," IEEE Access, vol. 12, pp. 26896–26909, 2024.
2. 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," Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.
3. J. Redmon and A. Farhadi, "YOLOv3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018.
4. F. Chollet, Deep Learning with Python. Manning Publications Co., 2017.
5. I. Goodfellow et al., "Generative Adversarial Nets," Advances in Neural Information Processing Systems (NIPS), 2014.
6. J. Ho, A. Jain, and S. Abbeel, "Denoising Diffusion Probabilistic Models," Advances in Neural Information Processing Systems (NeurIPS), 2020.
7. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
8. A. Adadi and M. Berrada, "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)," IEEE Access, vol. 6, pp. 52138-52160, 2018.
9. C. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, E. Thies, and M. Nießner, "FaceForensics++: Learning to Detect Manipulated Facial Images," Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2019.
10. R. Durall, M. Keuper, J. P. Ebehard, S. P. A. F. El-Attar, and A. Keuper, "Frequency Analysis of Generated Images," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020.
2. 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," Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.
3. J. Redmon and A. Farhadi, "YOLOv3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018.
4. F. Chollet, Deep Learning with Python. Manning Publications Co., 2017.
5. I. Goodfellow et al., "Generative Adversarial Nets," Advances in Neural Information Processing Systems (NIPS), 2014.
6. J. Ho, A. Jain, and S. Abbeel, "Denoising Diffusion Probabilistic Models," Advances in Neural Information Processing Systems (NeurIPS), 2020.
7. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
8. A. Adadi and M. Berrada, "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)," IEEE Access, vol. 6, pp. 52138-52160, 2018.
9. C. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, E. Thies, and M. Nießner, "FaceForensics++: Learning to Detect Manipulated Facial Images," Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2019.
10. R. Durall, M. Keuper, J. P. Ebehard, S. P. A. F. El-Attar, and A. Keuper, "Frequency Analysis of Generated Images," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020.
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