Git-Mirrors/tensorflow-for-deep-learning-py
1
0
Fork 0
mirror of https://github.com/autistic-symposium/tensorflow-for-deep-learning-py.git synced 2025-05-10 10:45:04 -04:00
Code Issues Projects Releases Packages Wiki Activity

change author

Browse source
This commit is contained in:
Marina von Steinkirch 2016-07-23 17:12:58 -07:00
parent 8487607501
commit 7cc5fc0c92
1 changed files with 9 additions and 5 deletions
Download patch file Download diff file Expand all files Collapse all files

14
Papers/Adversarial_examples_1607.02533v1.md
View file

@ -1,8 +1,12 @@
# Intro
# Adversarial Examples in the Physical World
* Predictive or Supervised: learn a mappping from inputs x to outputs u, given a labeled set of input-output paris (the training set).
- The training input x_i is called features, attributes, covariates.
- If y_i assumes a value from a finite set, it's called categorical or nominal, and the problem is classification or pattern recognition. If y_i us real-valued scalar, it is regression.
## Kurakin, Goodfellow, Bengio
http://arxiv.org/pdf/1607.02533v1.pdf
* Descriptive or unsupervised learning: find patterns in the data (knowledge discovery). c
* An adversarial example is a sample of input data which has been modified
very slightly in a way that is intended to cause a machine learning classifier
to misclassify it.
* Adversarial examples pose security concerns because they could be
used to perform an attack on machine learning systems, even if the adversary has
no access to the underlying model