Unveiling Universal Forensics of Diffusion Models with Adversarial Perturbations-Reference-Cited by-同舟云学术

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Unveiling Universal Forensics of Diffusion Models with Adversarial Perturbations

Published:2024-06-30 Issue: Volume:33 Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
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Author:

Xie Kangyang¹,Liu Jiaan¹,Zhu Muzhi¹,Ding Ganggui¹,Liu Zide¹,Chen Hao¹,Chen Hangyue²

Affiliation:

1. Zhejiang University,College of Computer Science and Technology,China

2. Hangzhou Dianzi University,Department of Digital Media Art,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650754.pdf?arnumber=10650754

Reference29 articles.

1. Denoising diffusion probabilistic models;Ho;Advances in Neural Information Processing Systems,2020

2. Diffusion models beat gans on image synthesis;Dhariwal;Advances in Neural Information Processing Systems,2021

3. Hierarchical text-conditional image generation with clip latents;Ramesh,2022

4. Photorealistic text-to-image diffusion models with deep language understanding;Saharia,2022

5. AI-synthesized faces are indistinguishable from real faces and more trustworthy

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