Universal Adversarial Perturbations (UAP) 很“便宜” - 单个噪声可用于导致模型错误标记大量图像。(与基于每个图像生成扰动的通常攻击不同。但这些更有效)。论文还发现 UAP 可以跨不同模型,因此它们也可以用于黑盒攻击设置,因此研究它们很重要。 UAP vs Adversarial Perturbation:为了攻击给定的模型,在一个常见的对抗...
In addition, we also demonstrate how universal adversarial training benefits the robustness of the model against universal attacks. We release our tool GUAP on https://github.com/TrustAI/GUAP.doi:10.1007/s10994-023-06306-zYanghao ZhangWenjie Ruan...
In this paper, for the first time we propose a unified and flexible framework, which can capture the distribution of the unknown additive and non-additive adversarial perturbations jointly for crafting Generalized Universal Adversarial Perturbations. Specifically, GUAP can generate either additive (i.e....
"Universal Adversarial Robustness of Texture and Shape-Biased Models"(ICIP'21) "Robustness and Transferability of Universal Attacks on Compressed Models"(AAAI'21 Workshop) We encourage you to explore these Python notebooks to generate and evaluate your own UAPs. If you are new to this topic, we...
本文发表在AAAI2023,有完整代码,据github的项目所说还是Oral,即上台做pre的,应该是一篇好论文。一作来自厦门大学。 Abstract The ensemble attack with average weights can be leveraged for increasing the transferability of universal adversarial perturbation (UAP) by training with multiple Convolutional Neural Netw...
Therefore, it produces more successful attacks when the number of training samples is limited. Moreover, we provide a proof that the proposed penalty method theoretically converges to a solution that corresponds to universal adversarial perturbations. We also demonstrate that it is possible to provide...
It should be noted that, in [19], adversarial perturbations were previously shown to generalize well, to some extent, across different neural networks on the MNIST problem. Our results are however different, as we show the generalizability of universal perturbations across different architectures on ...
CVPR2017,原文链接:Universal adversarial perturbations 官方开源代码: github.com/LTS4/univers 请参阅此出版物的讨论、统计数据和作者简介:https://www.researchgate.net/publication/309460742 摘要 给定一个最先进的深度神经网络分类器,作者证明存在了一种通用的,图像不可知的和非常小的扰动向量,这会导致分类器以很高...
main 1BranchTags Code README MIT license LLM Attacks This is the official repository for "Universal and Transferable Adversarial Attacks on Aligned Language Models" byAndy Zou,Zifan Wang,Nicholas Carlini,Milad Nasr,J. Zico Kolter, andMatt Fredrikson. ...
uap: Generate universal adversarial pertubation Encoder Training main: Train and test of framework Citation Please cite this work if you find it useful: @inproceedings{peng2022fingerprinting, title={Fingerprinting deep neural networks globally via universal adversarial perturbations}, author={Peng, Zirui...