A General Framework For Detecting Anomalous Inputs to DNN Classifiers

29 July 2020

Papers citing "A General Framework For Detecting Anomalous Inputs to DNN Classifiers"

7 / 7 papers shown

Title
Adversarial Detection with a Dynamically Stable System Xiaowei Long Jie Lin Xiangyuan Yang AAML 41 0 0 11 Nov 2024
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation Fan Yin Yao Li Cho-Jui Hsieh Kai-Wei Chang AAML 69 4 0 22 Oct 2022
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection Nikolaos Dionelis Mehrdad Yaghoobi Sotirios A. Tsaftaris OODD 19 4 0 30 Nov 2021
SoK: Machine Learning Governance Varun Chandrasekaran Hengrui Jia Anvith Thudi Adelin Travers Mohammad Yaghini Nicolas Papernot 38 16 0 20 Sep 2021
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them Florian Tramèr AAML 30 65 0 24 Jul 2021
Anomaly Detection by Recombining Gated Unsupervised Experts Jan-Philipp Schulze Philip Sperl Konstantin Böttinger 29 1 0 31 Aug 2020
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,113 0 04 Nov 2016