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ARCHIVEMONTHLY»MARCH2019«
29
th
MARCH2019
READING NOTES
Amirata Ghorbani, Abubakar Abid, James Y. Zou.
Interpretation of Neural Networks is Fragile
. CoRR abs/1710.10547 (2017).
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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27
th
MARCH2019
READING NOTES
Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, Dawn Song.
Spatially Transformed Adversarial Examples.
CoRR abs/1801.02612 (2018).
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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25
th
MARCH2019
READING NOTES
Xi Wu, Uyeong Jang, Lingjiao Chen, Somesh Jha.
The Manifold Assumption and Defenses Against Adversarial Perturbations
OpenReview, 2018. https://openreview.net/forum?id=Hk-FlMbAZ.
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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22
th
MARCH2019
READING NOTES
Yash Sharma, Pin-Yu Chen.
Attacking the Madry Defense Model with L1-based Adversarial Examples
. CoRR abs/1710.10733 (2017).
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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20
th
MARCH2019
READING NOTES
Beranger Dumont, Simona Maggio, Pablo Montalvo.
Robustness of Rotation-Equivariant Networks to Adversarial Perturbations
. CoRR abs/1802.06627 (2018).
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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18
th
MARCH2019
READING NOTES
Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard.
Geometric Robustness of Deep Networks: Analysis and Improvement
. CVPR, 2018.
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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15
th
MARCH2019
READING NOTES
Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, Justin Gilmer.
Adversarial Patch
. CoRR abs/1712.09665 (2017).
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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13
th
MARCH2019
READING NOTES
Anish Athalye, Nicholas Carlini, David A. Wagner.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
. ICML, 2018.
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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11
th
MARCH2019
READING NOTES
Rima Alaifari, Giovanni S. Alberti, Tandri Gauksson.
ADef: an Iterative Algorithm to Construct Adversarial Deformations
. CoRR abs/1804.07729 (2018).
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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08
th
MARCH2019
READING NOTES
Joachim Folz, Sebastian Palacio, Jörn Hees, Damian Borth, Andreas Dengel.
Adversarial Defense based on Structure-to-Signal Autoencoders
. CoRR, abs/1803.07994, 2018.
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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