Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Hu, Yuepeng"'
Multimodal large language models (MLLMs) have become the cornerstone of today's generative AI ecosystem, sparking intense competition among tech giants and startups. In particular, an MLLM generates a text response given a prompt consisting of an ima
Externí odkaz:
http://arxiv.org/abs/2407.09050
Generative AI raises many societal concerns such as boosting disinformation and propaganda campaigns. Watermarking AI-generated content is a key technology to address these concerns and has been widely deployed in industry. However, watermarking is v
Externí odkaz:
http://arxiv.org/abs/2407.04086
Autor:
Cheng, Haoran, Peng, Liang, Xia, Linxuan, Hu, Yuepeng, Li, Hengjia, Lu, Qinglin, He, Xiaofei, Wu, Boxi
Significant advancements in video diffusion models have brought substantial progress to the field of text-to-video (T2V) synthesis. However, existing T2V synthesis model struggle to accurately generate complex motion dynamics, leading to a reduction
Externí odkaz:
http://arxiv.org/abs/2406.03215
Watermark has been widely deployed by industry to detect AI-generated images. A recent watermarking framework called \emph{Stable Signature} (proposed by Meta) roots watermark into the parameters of a diffusion model's decoder such that its generated
Externí odkaz:
http://arxiv.org/abs/2405.07145
Several companies--such as Google, Microsoft, and OpenAI--have deployed techniques to watermark AI-generated content to enable proactive detection. However, existing literature mainly focuses on user-agnostic detection. Attribution aims to further tr
Externí odkaz:
http://arxiv.org/abs/2404.04254
Watermark has been widely deployed by industry to detect AI-generated images. The robustness of such watermark-based detector against evasion attacks in the white-box and black-box settings is well understood in the literature. However, the robustnes
Externí odkaz:
http://arxiv.org/abs/2403.15365
Data poisoning attacks spoof a recommender system to make arbitrary, attacker-desired recommendations via injecting fake users with carefully crafted rating scores into the recommender system. We envision a cat-and-mouse game for such data poisoning
Externí odkaz:
http://arxiv.org/abs/2303.14601
Autor:
Xie, Xiaochun, Liu, Yang, Yang, Qi, Ma, Xiaojie, Lu, Yingying, Hu, Yuepeng, Zhang, Guofu, Ke, Lu, Tong, Zhihui, Liu, Yuxiu, Xue, Jing, Lu, Guotao, Li, Weiqin
Publikováno v:
In The American Journal of Pathology August 2024 194(8):1494-1510
Autor:
Hu, Yuepeng1 (AUTHOR), Chen, Jian-Min2 (AUTHOR), Zuo, Han3 (AUTHOR), Pu, Na1 (AUTHOR), Zhang, Guofu1 (AUTHOR), Duan, Yichen1 (AUTHOR), Li, Gang1 (AUTHOR), Tong, Zhihui1 (AUTHOR), Li, Weiqin1 (AUTHOR), Li, Baiqiang1,4 (AUTHOR) li_baiqiang@aliyun.com, Yang, Qi1,5 (AUTHOR) yangqi_nj@163.com
Publikováno v:
Lipids in Health & Disease. 4/1/2024, Vol. 23 Issue 1, p1-10. 10p.
Autor:
Peng, Hao, He, YuanPeng, Hu, Yuepeng, Sheng, Siqi, Maitiyasen, Maierhaba, Li, jingfeng, Liu, Yvxuan, Hou, Xinyu, Song, Haizhu, Yi, Jun
Publikováno v:
In Biomedicine & Pharmacotherapy October 2024 179