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pro vyhledávání: '"Yang, Tianyun"'
Despite the impressive capabilities of generating images, text-to-image diffusion models are susceptible to producing undesirable outputs such as NSFW content and copyrighted artworks. To address this issue, recent studies have focused on fine-tuning
Externí odkaz:
http://arxiv.org/abs/2405.16534
Nowadays, generative models are shaping various fields such as art, design, and human-computer interaction, yet accompanied by challenges related to copyright infringement and content management. In response, existing research seeks to identify the u
Externí odkaz:
http://arxiv.org/abs/2307.15977
Despite the remarkable progress in generative technology, the Janus-faced issues of intellectual property protection and malicious content supervision have arisen. Efforts have been paid to manage synthetic images by attributing them to a set of pote
Externí odkaz:
http://arxiv.org/abs/2303.06877
With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images. Existing works on fake image attribution perform multi-class classification on several Generative Adversarial Network (GAN) models and o
Externí odkaz:
http://arxiv.org/abs/2202.13843
Rapid pace of generative models has brought about new threats to visual forensics such as malicious personation and digital copyright infringement, which promotes works on fake image attribution. Existing works on fake image attribution mainly rely o
Externí odkaz:
http://arxiv.org/abs/2106.08749
Publikováno v:
Disinformation, Misinformation, and Fake News in Social Media. 2020
The increasing popularity of social media promotes the proliferation of fake news, which has caused significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging research area of great concern.
Externí odkaz:
http://arxiv.org/abs/2003.05096
The increasing popularity of social media promotes the proliferation of fake news. With the development of multimedia technology, fake news attempts to utilize multimedia contents with images or videos to attract and mislead readers for rapid dissemi
Externí odkaz:
http://arxiv.org/abs/1908.04472
Publikováno v:
In International Immunopharmacology June 2023 119
Akademický článek
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Autor:
Yang, Tianyun
This is the dataset for the paper "Progressive Open Space Expansion for Open-Set Model Attribution", CVPR2023
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7f97494b2d7446c7cb4f8c203daa21cd