Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Lin, Chenhao P."'
Publikováno v:
in Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, 2024, pp. 413-421
Detecting synthetic from real speech is increasingly crucial due to the risks of misinformation and identity impersonation. While various datasets for synthetic speech analysis have been developed, they often focus on specific areas, limiting their u
Externí odkaz:
http://arxiv.org/abs/2412.09032
Targeted poisoning attacks aim to compromise the model's prediction on specific target samples. In a common clean-label setting, they are achieved by slightly perturbing a subset of training samples given access to those specific targets. Despite con
Externí odkaz:
http://arxiv.org/abs/2412.03908
Autor:
Sun, Zhen, Cong, Tianshuo, Liu, Yule, Lin, Chenhao, He, Xinlei, Chen, Rongmao, Han, Xingshuo, Huang, Xinyi
Fine-tuning is an essential process to improve the performance of Large Language Models (LLMs) in specific domains, with Parameter-Efficient Fine-Tuning (PEFT) gaining popularity due to its capacity to reduce computational demands through the integra
Externí odkaz:
http://arxiv.org/abs/2411.17453
Code Language Models (CLMs) have achieved tremendous progress in source code understanding and generation, leading to a significant increase in research interests focused on applying CLMs to real-world software engineering tasks in recent years. Howe
Externí odkaz:
http://arxiv.org/abs/2411.07597
Deep generative models have demonstrated impressive performance in various computer vision applications, including image synthesis, video generation, and medical analysis. Despite their significant advancements, these models may be used for malicious
Externí odkaz:
http://arxiv.org/abs/2407.10575
Autor:
Yang, Yulong, Yang, Xinshan, Li, Shuaidong, Lin, Chenhao, Zhao, Zhengyu, Shen, Chao, Zhang, Tianwei
The rapid progress in the reasoning capability of the Multi-modal Large Language Models (MLLMs) has triggered the development of autonomous agent systems on mobile devices. MLLM-based mobile agent systems consist of perception, reasoning, memory, and
Externí odkaz:
http://arxiv.org/abs/2407.09295
In recent years, software systems powered by deep learning (DL) techniques have significantly facilitated people's lives in many aspects. As the backbone of these DL systems, various DL libraries undertake the underlying optimization and computation.
Externí odkaz:
http://arxiv.org/abs/2404.17871
Deep learning-based monocular depth estimation (MDE), extensively applied in autonomous driving, is known to be vulnerable to adversarial attacks. Previous physical attacks against MDE models rely on 2D adversarial patches, so they only affect a smal
Externí odkaz:
http://arxiv.org/abs/2403.17301
Autor:
Yang, Bo, Zhang, Hengwei, Wang, Jindong, Yang, Yulong, Lin, Chenhao, Shen, Chao, Zhao, Zhengyu
Transferable adversarial examples cause practical security risks since they can mislead a target model without knowing its internal knowledge. A conventional recipe for maximizing transferability is to keep only the optimal adversarial example from a
Externí odkaz:
http://arxiv.org/abs/2402.18370
Deep learning methods can not only detect false data injection attacks (FDIA) but also locate attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep learning vulnerabilities have been studied in the field of single-l
Externí odkaz:
http://arxiv.org/abs/2401.16001