Universal Detection and Source Attribution of Diffusion Model Generated Images with High Generalization and Robustness-Reference-Cited by-同舟云学术

Universal Detection and Source Attribution of Diffusion Model Generated Images with High Generalization and Robustness

Published:2023 Issue: Volume: Page:441-448
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Das Sanandita^ORCID,Dutta Dibyarup^ORCID,Ghosh Tanusree^ORCID,Naskar Ruchira^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-45170-6_45

Reference27 articles.

1. Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)

2. Lago, F., Pasquini, C., Böhme, R., Dumont, H., Goffaux, V., Boato, G.: More real than real: a study on human visual perception of synthetic faces [applications corner]. IEEE Signal Process. Mag. 39(1), 109–116 (2021)

3. Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of styleGAN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110–8119 (2020)

4. Hancock, J.T., Bailenson, J.N.: The social impact of DeepFakes. Cyberpsychol. Behav. Soc. Netw. 24(3), 149–152 (2021). PMID: 33760669

5. Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: Advances in Neural Information Processing, vol. 34, pp. 8780–8794 (2021)

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