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The generative model has made significant advancements in the creation of realistic videos, which causes security issues.
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2022
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2022
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C. Li, Z. Zhang, H. Wu, W. Sun, X. Min, X. Liu, G. Zhai, and W. Lin, “AGIQA-3K: An open database for AI-generated image quality assessment,” IEEE Trans. Circuits Syst. Video Technol. , Early Access, 2023
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Y. Liu, L. Li, S. Ren, R. Gao, S. Li, S. Chen, X. Sun, and L. Hou, “Fetv: A benchmark for fine-grained evaluation of open-domain text-to-video generation,” in Proc. Adv. Neural Inf. Process. Syst. , vol. 36, 2023, pp. 62 352–62 387
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L. Khachatryan, A. Movsisyan, V. Tadevosyan, R. Henschel, Z. Wang, S. Navasardyan, and H. Shi, “Text2video-zero: Text-to-image diffusion models are zero-shot video generators,” in Proc. IEEE Int. Conf. Comput. Vis. , 2023, pp. 15 954–15 964
2023
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2023
Cited alongside, same era.
Alivilab, “Ali-vilab, https://huggingface.co/ali-vilab/text-to-video-ms-1.7b
2023
Cited alongside, same era.
M. Zhu, H. Chen, Q. Yan, X. Huang, G. Lin, W. Li, Z. Tu, H. Hu, J. Hu, and Y. Wang, “Genimage: A million-scale benchmark for detecting AI-generated image,” Proc. Adv. Neural Inf. Process. Syst. , vol. 36, 2023
2023
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2023
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H. Chen, Y. Zhang, X. Wang, X. Duan, Y. Zhou, and W. Zhu, “DisenDreamer: Subject-driven text-to-image generation with sample-aware disentangled tuning,” IEEE Trans. Circuits Syst. Video Technol. , Early Access, 2024
2024
Closest in time.
X. Chen, J. Tan, T. Wang, K. Zhang, W. Luo, and X. Cao, “Towards real-world blind face restoration with generative diffusion prior,” IEEE Trans. Circuits Syst. Video Technol. , Early Access, 2024
2024
Closest in time.
C. Tan, H. Liu, Y. Zhao, S. Wei, G. Gu, P. Liu, and Y. Wei, “Rethinking the up-sampling operations in CNN-based generative network for generalizable deepfake detection,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , Accepted, 2024
2024
Closest in time.