Zobrazeno 1 - 10
of 1 325
pro vyhledávání: '"Pang, Yan"'
Detecting multimodal misinformation, especially in the form of image-text pairs, is crucial. Obtaining large-scale, high-quality real-world fact-checking datasets for training detectors is costly, leading researchers to use synthetic datasets generat
Externí odkaz:
http://arxiv.org/abs/2409.19656
Video generation models (VGMs) have demonstrated the capability to synthesize high-quality output. It is important to understand their potential to produce unsafe content, such as violent or terrifying videos. In this work, we provide a comprehensive
Externí odkaz:
http://arxiv.org/abs/2407.12581
Federated learning (FL) allows multiple devices to train a model collaboratively without sharing their data. Despite its benefits, FL is vulnerable to privacy leakage and poisoning attacks. To address the privacy concern, secure aggregation (SecAgg)
Externí odkaz:
http://arxiv.org/abs/2405.15182
Wasserstein distance is a principle measure of data divergence from a distributional standpoint. However, its application becomes challenging in the context of data privacy, where sharing raw data is restricted. Prior attempts have employed technique
Externí odkaz:
http://arxiv.org/abs/2404.06787
Publikováno v:
JMIR mHealth and uHealth, Vol 8, Iss 3, p e15702 (2020)
BackgroundAs people living with HIV infection require lifelong treatment, nonadherence to medication will reduce their chance of maintaining viral suppression and increase the risk of developing drug resistance and HIV transmission. ObjectiveThis st
Externí odkaz:
https://doaj.org/article/07c44c43499b49ddb144a4b00e19ff9e
With the rapid advancement in video generation, people can conveniently utilize video generation models to create videos tailored to their specific desires. Nevertheless, there are also growing concerns about their potential misuse in creating and di
Externí odkaz:
http://arxiv.org/abs/2402.13126
We investigate a generalized poly-Laplacian system with a parameter on weighted finite graph, a generalized poly-Laplacian system with a parameter and Dirichlet boundary value on weighted locally finite graphs, and a $(p,q)$-Laplacian system with a p
Externí odkaz:
http://arxiv.org/abs/2401.15372
State-of-the-art large language models (LLMs) are typically deployed as online services, requiring users to transmit detailed prompts to cloud servers. This raises significant privacy concerns. In response, we introduce ConfusionPrompt, a novel frame
Externí odkaz:
http://arxiv.org/abs/2401.00870
Autor:
Pang, Yan, Wang, Tianhao
With the rapid advancement of diffusion-based image-generative models, the quality of generated images has become increasingly photorealistic. Moreover, with the release of high-quality pre-trained image-generative models, a growing number of users a
Externí odkaz:
http://arxiv.org/abs/2312.08207
Federated Learning (FL) enables collaborative model training while preserving the privacy of raw data. A challenge in this framework is the fair and efficient valuation of data, which is crucial for incentivizing clients to contribute high-quality da
Externí odkaz:
http://arxiv.org/abs/2311.05304