ARCHIVES
Year 2026 · Volume 7 · Issue 4
Original Article
Deep Learning-Based Detection of AI-Generated Images Using Spatial and Frequency-Domain Features
Yateesh Kumar. V1
1 MCA student, School of science and Computer Studies, CMR University, Bangalore, Karnataka, India.
Published Online: July-August 2026
Pages: 258-261
Cite this article
No DOIReferences
1. F. Marra, D. Gragnaniello, L. Verdoliva, and G. Poggi, “Do GANs leave artificial fingerprints?” MIPR, 2019.
2. N. Yu, L. S. Davis, and M. Fritz, “Attributing fake images to GANs: Learning and analyzing GAN fingerprints,” ICCV, 2019.
3. Y. Li and S. Lyu, “Exposing DeepFake videos by detecting face warping artifacts,” CVPRW, 2019.
4. Rössler et al., “FaceForensics++: Learning to detect manipulated facial images,” ICCV, 2019.
5. L. Nataraj et al., “Detecting GAN generated fake images using co-occurrence matrices,” 2019.
6. S.-Y. Wang et al., “CNN-generated images are surprisingly easy to spot…for now,” CVPR, 2020.
7. J. Frank and T. Holz, “[RE] CNN-generated images are surprisingly easy to spot…for now,” 2021.
8. Y. Ju et al., “Fusing global and local features for generalized AI-synthesized image detection,” ICIP, 2022.
9. U. Ojha, Y. Li, and Y. J. Lee, “Towards universal fake image detectors that generalize across generative models,” CVPR, 2023.
10. M. Zhu et al., “GenImage: A million-scale benchmark for detecting AI-generated image,” NeurIPS, 2023.
11. Z. Wang et al., “DIRE for diffusion-generated image detection,” ICCV, 2023.
12. J. Ricker, D. Lukovnikov, and A. Fischer, “AEROBLADE: Training-free detection of latent diffusion images using autoencoder reconstruction error,” CVPR, 2024.
13. Chen et al., “DRCT: Diffusion reconstruction contrastive training towards universal detection of diffusion generated images,” ICML, 2024.
14. G. Cazenavette et al., “FakeInversion: Learning to detect images from unseen text-to-image models by inverting Stable Diffusion,” CVPR, 2024.
15. S. Yan et al., “A sanity check for AI-generated image detection,” ICLR, 2025.
16. Tan et al., “C2P-CLIP: Injecting category common prompt in CLIP to enhance generalization in deepfake detection,” AAAI, 2025.
17. F. Marra et al., “Incremental learning for the detection and classification of GAN-generated images,” WIFS, 2019.
18. H. Lin et al., “Revisiting DIRE: Towards universal AI-generated image detection,” Neural Networks, 2026.
2. N. Yu, L. S. Davis, and M. Fritz, “Attributing fake images to GANs: Learning and analyzing GAN fingerprints,” ICCV, 2019.
3. Y. Li and S. Lyu, “Exposing DeepFake videos by detecting face warping artifacts,” CVPRW, 2019.
4. Rössler et al., “FaceForensics++: Learning to detect manipulated facial images,” ICCV, 2019.
5. L. Nataraj et al., “Detecting GAN generated fake images using co-occurrence matrices,” 2019.
6. S.-Y. Wang et al., “CNN-generated images are surprisingly easy to spot…for now,” CVPR, 2020.
7. J. Frank and T. Holz, “[RE] CNN-generated images are surprisingly easy to spot…for now,” 2021.
8. Y. Ju et al., “Fusing global and local features for generalized AI-synthesized image detection,” ICIP, 2022.
9. U. Ojha, Y. Li, and Y. J. Lee, “Towards universal fake image detectors that generalize across generative models,” CVPR, 2023.
10. M. Zhu et al., “GenImage: A million-scale benchmark for detecting AI-generated image,” NeurIPS, 2023.
11. Z. Wang et al., “DIRE for diffusion-generated image detection,” ICCV, 2023.
12. J. Ricker, D. Lukovnikov, and A. Fischer, “AEROBLADE: Training-free detection of latent diffusion images using autoencoder reconstruction error,” CVPR, 2024.
13. Chen et al., “DRCT: Diffusion reconstruction contrastive training towards universal detection of diffusion generated images,” ICML, 2024.
14. G. Cazenavette et al., “FakeInversion: Learning to detect images from unseen text-to-image models by inverting Stable Diffusion,” CVPR, 2024.
15. S. Yan et al., “A sanity check for AI-generated image detection,” ICLR, 2025.
16. Tan et al., “C2P-CLIP: Injecting category common prompt in CLIP to enhance generalization in deepfake detection,” AAAI, 2025.
17. F. Marra et al., “Incremental learning for the detection and classification of GAN-generated images,” WIFS, 2019.
18. H. Lin et al., “Revisiting DIRE: Towards universal AI-generated image detection,” Neural Networks, 2026.
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