Defending via strategic ML selection

Guy Barash
Guy Barash

arXiv: Cryptography and Security, 2019.

Cited by: 0|Bibtex|Views4
EI
Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

The results of a learning process depend on the input data. There are cases in which an adversary can strategically tamper with the input data to affect the outcome of the learning process. While some datasets are difficult to attack, many others are susceptible to manipulation. A resourceful attacker can tamper with large portions of the...More

Code:

Data:

Full Text
Your rating :
0

 

Tags
Comments