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Advancements in deep image synthesis techniques, such as generative adversarial networks (GANs) and diffusion models (DMs), have ushered in an era of generating highly realistic images.
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W. Li, P. Zhang, L. Zhang, Q. Huang, X. He, S. Lyu, and J. Gao, “Object-driven text-to-image synthesis via adversarial training,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 174–12 182
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2020
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
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2020
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2020
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X. Zhu, L. Zhang, L. Zhang, X. Liu, Y. Shen, and S. Zhao, “Gan-based image super-resolution with a novel quality loss,” Mathematical Problems in Engineering , vol. 2020, pp. 1–12, 2020
2020
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D. M. Souza, J. Wehrmann, and D. D. Ruiz, “Efficient neural architecture for text-to-image synthesis,” in 2020 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2020, pp. 1–8
2020
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Z. Wang, Z. Quan, Z.-J. Wang, X. Hu, and Y. Chen, “Text to image synthesis with bidirectional generative adversarial network,” in 2020 IEEE International Conference on Multimedia and Expo (ICME) . IEEE, 2020, pp. 1–6
2020
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H. Li, B. Li, S. Tan, and J. Huang, “Identification of deep network generated images using disparities in color components,” Signal Processing , vol. 174, p. 107616, 2020
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2021
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2022
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2022
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2023
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2023
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R. Corvi, D. Cozzolino, G. Zingarini, G. Poggi, K. Nagano, and L. Verdoliva, “On the detection of synthetic images generated by diffusion models,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
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Y. Ju, S. Jia, J. Cai, H. Guan, and S. Lyu, “Glff: Global and local feature fusion for ai-synthesized image detection,” IEEE Transactions on Multimedia , 2023
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2023
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X. Guo, X. Liu, Z. Ren, S. Grosz, I. Masi, and X. Liu, “Hierarchical fine-grained image forgery detection and localization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2023, pp. 3155–3165
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2023
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2023
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U. Ojha, Y. Li, and Y. J. Lee, “Towards universal fake image detectors that generalize across generative models,” 2023
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H. Liz-López, M. Keita, A. Taleb-Ahmed, A. Hadid, J. Huertas-Tato, and D. Camacho, “Generation and detection of manipulated multimodal audiovisual content: Advances, trends and open challenges,” Information Fusion , vol. 103, p. 102103, 2024
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