Zobrazeno 1 - 10
of 624
pro vyhledávání: '"Wei, Zhipeng"'
Autor:
Gao, Ziyi, Chen, Kai, Wei, Zhipeng, Mou, Tingshu, Chen, Jingjing, Tan, Zhiyu, Li, Hao, Jiang, Yu-Gang
Recent diffusion-based unrestricted attacks generate imperceptible adversarial examples with high transferability compared to previous unrestricted attacks and restricted attacks. However, existing works on diffusion-based unrestricted attacks are mo
Externí odkaz:
http://arxiv.org/abs/2408.05479
Text-to-image models encounter safety issues, including concerns related to copyright and Not-Safe-For-Work (NSFW) content. Despite several methods have been proposed for erasing inappropriate concepts from diffusion models, they often exhibit incomp
Externí odkaz:
http://arxiv.org/abs/2407.12383
In this paper, we propose a novel transfer-based targeted attack method that optimizes the adversarial perturbations without any extra training efforts for auxiliary networks on training data. Our new attack method is proposed based on the observatio
Externí odkaz:
http://arxiv.org/abs/2209.03716
Publikováno v:
In Optics and Laser Technology September 2024 176
Publikováno v:
In Optics and Laser Technology August 2024 175
Recent studies have shown that adversarial examples hand-crafted on one white-box model can be used to attack other black-box models. Such cross-model transferability makes it feasible to perform black-box attacks, which has raised security concerns
Externí odkaz:
http://arxiv.org/abs/2112.05379
Recent research has demonstrated that Deep Neural Networks (DNNs) are vulnerable to adversarial patches which introduce perceptible but localized changes to the input. Nevertheless, existing approaches have focused on generating adversarial patches o
Externí odkaz:
http://arxiv.org/abs/2110.15629
Although deep-learning based video recognition models have achieved remarkable success, they are vulnerable to adversarial examples that are generated by adding human-imperceptible perturbations on clean video samples. As indicated in recent studies,
Externí odkaz:
http://arxiv.org/abs/2110.09075
Autor:
Kang, Yubin, Meng, Bingheng, Hou, Xiaobing, Wang, Puning, Tang, Jilong, Wang, Le, Wei, Zhipeng
Publikováno v:
In Vacuum October 2024 228
Vision transformers (ViTs) have demonstrated impressive performance on a series of computer vision tasks, yet they still suffer from adversarial examples. % crafted in a similar fashion as CNNs. In this paper, we posit that adversarial attacks on tra
Externí odkaz:
http://arxiv.org/abs/2109.04176