LAFEAT: Piercing Through Adversarial Defenses with Latent Features

19 April 2021

Papers citing "LAFEAT: Piercing Through Adversarial Defenses with Latent Features"

9 / 9 papers shown

Title
MTL-UE: Learning to Learn Nothing for Multi-Task Learning Yi Yu Song Xia Siyuan Yang Chenqi Kong Wenhan Yang Shijian Lu Yap-Peng Tan Alex Chichung Kot 46 0 0 08 May 2025
Towards Scalable Topological Regularizers Hiu-Tung Wong Darrick Lee Hong Yan BDL 57 0 0 24 Jan 2025
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Yong Xie Weijie Zheng Hanxun Huang Guangnan Ye Xingjun Ma AAML 72 1 0 20 Nov 2024
Certifying LLM Safety against Adversarial Prompting Aounon Kumar Chirag Agarwal Suraj Srinivas Aaron Jiaxun Li S. Feizi Himabindu Lakkaraju AAML 22 164 0 06 Sep 2023
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks Nikolaos Antoniou Efthymios Georgiou Alexandros Potamianos AAML 27 5 0 15 Dec 2022
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Ajmal Saeed Mian Navid Kardan M. Shah AAML 26 235 0 01 Aug 2021
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks Linhai Ma Liang Liang AAML 18 18 0 19 May 2020
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 256 3,109 0 04 Nov 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 250 5,833 0 08 Jul 2016