Zobrazeno 1 - 10
of 1 434
pro vyhledávání: '"Ren, Kui"'
We propose a policy iteration method to solve an inverse problem for a mean-field game (MFG) model, specifically to reconstruct the obstacle function in the game from the partial observation data of value functions, which represent the optimal costs
Externí odkaz:
http://arxiv.org/abs/2409.06184
This paper studies inverse problems in quantitative photoacoustic tomography with additional optical current data supplemented from diffuse optical tomography. We propose a three-stage image reconstruction method for the simultaneous recovery of the
Externí odkaz:
http://arxiv.org/abs/2408.03496
Autor:
Cheng, Peng, Wang, Yuwei, Huang, Peng, Ba, Zhongjie, Lin, Xiaodong, Lin, Feng, Lu, Li, Ren, Kui
Extensive research has revealed that adversarial examples (AE) pose a significant threat to voice-controllable smart devices. Recent studies have proposed black-box adversarial attacks that require only the final transcription from an automatic speec
Externí odkaz:
http://arxiv.org/abs/2408.01808
Autor:
Xu, Huiyu, Zhang, Wenhui, Wang, Zhibo, Xiao, Feng, Zheng, Rui, Feng, Yunhe, Ba, Zhongjie, Ren, Kui
Recently, advanced Large Language Models (LLMs) such as GPT-4 have been integrated into many real-world applications like Code Copilot. These applications have significantly expanded the attack surface of LLMs, exposing them to a variety of threats.
Externí odkaz:
http://arxiv.org/abs/2407.16667
Autor:
Yang, Yuchen, Yao, Hongwei, Yang, Bingrun, He, Yiling, Li, Yiming, Zhang, Tianwei, Qin, Zhan, Ren, Kui
Recently, code-oriented large language models (Code LLMs) have been widely and successfully used to simplify and facilitate code programming. With these tools, developers can easily generate desired complete functional codes based on incomplete code
Externí odkaz:
http://arxiv.org/abs/2407.09164
Autor:
Ma, Binhao, Zheng, Tianhang, Hu, Hongsheng, Wang, Di, Wang, Shuo, Ba, Zhongjie, Qin, Zhan, Ren, Kui
Machine learning models trained on vast amounts of real or synthetic data often achieve outstanding predictive performance across various domains. However, this utility comes with increasing concerns about privacy, as the training data may include se
Externí odkaz:
http://arxiv.org/abs/2407.05112
The rapid advancement of Text-to-Image(T2I) generative models has enabled the synthesis of high-quality images guided by textual descriptions. Despite this significant progress, these models are often susceptible in generating contents that contradic
Externí odkaz:
http://arxiv.org/abs/2406.16333
Autor:
Wu, Sifan, Liu, Zhenguang, Zhang, Beibei, Zimmermann, Roger, Ba, Zhongjie, Zhang, Xiaosong, Ren, Kui
Human motion copy is an intriguing yet challenging task in artificial intelligence and computer vision, which strives to generate a fake video of a target person performing the motion of a source person. The problem is inherently challenging due to t
Externí odkaz:
http://arxiv.org/abs/2406.16601
Federated Learning (FL) exhibits privacy vulnerabilities under gradient inversion attacks (GIAs), which can extract private information from individual gradients. To enhance privacy, FL incorporates Secure Aggregation (SA) to prevent the server from
Externí odkaz:
http://arxiv.org/abs/2406.15731
Autor:
Zheng, Yihao, Xia, Haocheng, Pang, Junyuan, Liu, Jinfei, Ren, Kui, Chu, Lingyang, Cao, Yang, Xiong, Li
Watermarking is broadly utilized to protect ownership of shared data while preserving data utility. However, existing watermarking methods for tabular datasets fall short on the desired properties (detectability, non-intrusiveness, and robustness) an
Externí odkaz:
http://arxiv.org/abs/2406.14841