When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time

18 December 2017

Papers citing "When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time"

9 / 9 papers shown

Title
TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems Islam Debicha Richard Bauwens Thibault Debatty Jean-Michel Dricot Tayeb Kenaza Wim Mees AAML 24 40 0 27 Oct 2022
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them Florian Tramèr AAML 30 65 0 24 Jul 2021
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers Xi Li David J. Miller Zhen Xiang G. Kesidis AAML 16 0 0 28 May 2021
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing Zhen Xiang David J. Miller G. Kesidis 35 22 0 15 Oct 2020
Anomalous Example Detection in Deep Learning: A Survey Saikiran Bulusu B. Kailkhura Bo-wen Li P. Varshney D. Song AAML 28 47 0 16 Mar 2020
Revealing Perceptible Backdoors, without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic Zhen Xiang David J. Miller Hang Wang G. Kesidis AAML 34 9 0 18 Nov 2019
Detection of Backdoors in Trained Classifiers Without Access to the Training Set Zhen Xiang David J. Miller G. Kesidis AAML 30 23 0 27 Aug 2019
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 50 226 0 18 Jul 2018
Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch João Monteiro Isabela Albuquerque Zahid Akhtar T. Falk AAML 46 29 0 21 Feb 2018