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since it is a known false positive: https://twitter.com/bsstoner/status/1085624406796365830
my theory is that DDG-default users tend to turn on first party fingerprinting protection, which makes th…
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It'd be nice to have an example for the demonstration
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Hello!
When I try to run sh super_resolution.sh,there is an error occured.
(pytorch) D:\Graduate\super-resolution-adversarial-defense-master\src>sh super_resolution.sh
Making model...
Download…
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Do you have any assumptions about the model population?
ajing updated
4 years ago
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**Defense**: {Bandlimiting Neural Networks Against Adversarial Attacks}
**Write-up**: {https://arxiv.org/abs/1912.00049}
**Authors**: {Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion,…
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|Epic Issue|
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|#3709|
## Description
Issue #3711 is intended to provide a new database to store all MITRE attack data updated. In order to make this data accessible to users, we must prov…
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Hi, I am trying to reproduce the result in table 8, and attack via cleverhans.
The accuracy of the classifier (with the same architecture of table 1) is above 99, but the white-box attack accuracy o…
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Can you give this [policy](http://highscalability.com/blog/2016/1/25/design-of-a-modern-cache.html) a try? I'd be interested in hearing feedback, concerns, ideas, etc.
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**[Error Message]**
Traceback (most recent call last):
File "detection_as_defense.py", line 67, in
transformationList)
File "/home/kevinsjh_gmail_com/adversarial_transformers/util.py", …
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Approach 1: add randomness (random noise) to AEs and then use them to train ensemble models
Approach 2: use the strongest type of AEs to build ensemble models for defense