Stateful Defenses for Machine Learning Models Are Not Yet Secure Against
Black-box Attacks

Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks

11 March 2023

Papers citing "Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks"

11 / 11 papers shown

Title
AdvMind: Inferring Adversary Intent of Black-Box Attacks Ren Pang Xinyang Zhang S. Ji Xiapu Luo Ting Wang MLAU AAML 32 30 0 16 Jun 2020
QEBA: Query-Efficient Boundary-Based Blackbox Attack Huichen Li Xiaojun Xu Xiaolu Zhang Shuang Yang Yue Liu AAML 96 181 0 28 May 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 198 1,821 0 03 Mar 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 206 827 0 19 Feb 2020
PDQ & TMK + PDQF -- A Test Drive of Facebook's Perceptual Hashing Algorithms Janis Dalins Campbell Wilson Douglas Boudry 22 11 0 16 Dec 2019
Square Attack: a query-efficient black-box adversarial attack via random search Maksym Andriushchenko Francesco Croce Nicolas Flammarion Matthias Hein AAML 59 977 0 29 Nov 2019
Stateful Detection of Black-Box Adversarial Attacks Steven Chen Nicholas Carlini D. Wagner AAML MLAU 46 120 0 12 Jul 2019
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack Jianbo Chen Michael I. Jordan Martin J. Wainwright AAML 56 661 0 03 Apr 2019
Adversarial Patch Tom B. Brown Dandelion Mané Aurko Roy Martín Abadi Justin Gilmer AAML 64 1,093 0 27 Dec 2017
Deep Learning for Identifying Metastatic Breast Cancer Dayong Wang A. Khosla Rishab Gargeya H. Irshad Andrew H. Beck MedIm 54 940 0 18 Jun 2016
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 1.2K 39,383 0 01 Sep 2014