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1706.06083
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Towards Deep Learning Models Resistant to Adversarial Attacks
19 June 2017
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILM
OOD
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Papers citing
"Towards Deep Learning Models Resistant to Adversarial Attacks"
50 / 6,612 papers shown
Title
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Multi-way Encoding for Robustness
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Adversarial Robustness as a Prior for Learned Representations
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82
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Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models
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Adversarially Robust Generalization Just Requires More Unlabeled Data
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Adversarial Examples for Edge Detection: They Exist, and They Transfer
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57
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Unlabeled Data Improves Adversarial Robustness
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Are Labels Required for Improving Adversarial Robustness?
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Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
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Interpretable Adversarial Training for Text
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Bandlimiting Neural Networks Against Adversarial Attacks
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42
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Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
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Functional Adversarial Attacks
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Certifiably Robust Interpretation in Deep Learning
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163
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Label Universal Targeted Attack
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Scaleable input gradient regularization for adversarial robustness
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Provable robustness against all adversarial
l
p
l_p
l
p
-perturbations for
p
≥
1
p\geq 1
p
≥
1
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Matthias Hein
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Distributionally Robust Optimization and Generalization in Kernel Methods
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Non-Determinism in Neural Networks for Adversarial Robustness
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