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Evading adversarial example detection defenses requires finding adversarial examples that must simultaneously (a) be misclassified by the model and (b) be detected as non-adversarial.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Ensemble classifiers for steganalysis of digital media
Jan Kodovsky, Jessica Fridrich, and Vojtěch Holub · 2012
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel̃ Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, and Rob Goodfellow, Ian an d Fergus · 2014
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Detecting adversarial samples from artifacts
Reuben Feinman, Ryan R Curtin, Saurabh Shintre, and Andrew B Gardner · 2017
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Adversarial attacks on neural network policies
Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, and Pieter Abbeel · 2017
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Magnet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
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On detecting adversarial perturbations
Jan Hendrik Metzen, Tim Genewein, Volker Fischer, and Bastian Bischoff · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Generating natural language adversarial examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani B. Srivastava, and Kai-Wei Chang · 2018
Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
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Detection based defense against adversarial examples from the steganalysis point of view
Jiayang Liu, Weiming Zhang, Yiwei Zhang, Dongdong Hou, Yujia Liu, Hongyue Zha, and Nenghai Yu · 2019
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The odds are odd: A statistical test for detecting adversarial examples
Kevin Roth, Yannic Kilcher, and Thomas Hofmann · 2019
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DLA: dense-layer-analysis for adversarial example detection
Philip Sperl, Ching-yu Kao, Peng Chen, and Konstantin Böttinger · 2019
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
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Cited alongside, same era.
Audio adversarial examples: Targeted attacks on speech-to-text
Nicholas Carlini and David Wagner · 2018
Cited alongside, same era.
Empirical study of the topology and geometry of deep networks
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, and Stefano Soatto · 2018
Cited alongside, same era.
Characterizing adversarial subspaces using local intrinsic dimensionality
Xingjun Ma, Bo Li, Yisen Wang, Sarah M Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E Houle, and James Bailey · 2018
Cited alongside, same era.
Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter · 2019
Cited alongside, same era.
Linxi Jiang, Xingjun Ma, Zejia Weng, James Bailey, and Yu-Gang Jiang · 2020
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Dla: Dense-layer-analysis for adversarial example detection
Philip Sperl, Ching-Yu Kao, Peng Chen, Xiao Lei, and Konstantin Böttinger · 2020
Later among the works it cites.
Gotta catch’em all: Using honeypots to catch adversarial attacks on neural networks
Shawn Shan, Emily Wenger, Bolun Wang, Bo Li, Haitao Zheng, and Ben Y Zhao · 2020
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On adaptive attacks to adversarial example defenses
Florian Tramèr, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
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Detecting adversarial examples from sensitivity inconsistency of spatial-transform domain
Jinyu Tian, Jiantao Zhou, Yuanman Li, and Jia Duan · 2021
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