DLA: Dense-Layer-Analysis for Adversarial Example Detection

5 November 2019

Papers citing "DLA: Dense-Layer-Analysis for Adversarial Example Detection"

8 / 8 papers shown

Title
AdvCheck: Characterizing Adversarial Examples via Local Gradient Checking Ruoxi Chen Haibo Jin Jinyin Chen Haibin Zheng AAML 49 0 0 25 Mar 2023
Adversarial Example Detection in Deployed Tree Ensembles Laurens Devos Wannes Meert Jesse Davis AAML 44 1 0 27 Jun 2022
What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction Yijun Yang Ruiyuan Gao Yu Li Qiuxia Lai Qiang Xu GAN AAML 106 20 0 24 Jan 2022
Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features Hui Liu Bo Zhao Minzhi Ji Yuefeng Peng Jiabao Guo Peng Liu AAML 56 2 0 19 Jul 2021
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent Oliver Bryniarski Nabeel Hingun Pedro Pachuca Vincent Wang Nicholas Carlini AAML 82 37 0 28 Jun 2021
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart Tianyu Pang Huishuai Zhang Di He Yinpeng Dong Hang Su Wei Chen Jun Zhu Tie-Yan Liu AAML 45 18 0 31 May 2021
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels Jingfeng Zhang Xilie Xu Bo Han Tongliang Liu Gang Niu Li-zhen Cui Masashi Sugiyama NoLa AAML 87 9 0 31 May 2021
Optimizing Information Loss Towards Robust Neural Networks Philip Sperl Konstantin Böttinger AAML 45 3 0 07 Aug 2020