Adversarial machine learning

Adversarial machine learning is about studying attacks on machine learning systems and finding ways to protect them. A survey in May 2020 showed that experts want better security for machine learning in real-world use.

Most machine learning methods are made for specific tasks and assume that training and test data come from the same pattern. But in real life, especially in important applications, this is not always true. People might give false data on purpose to trick the system.

References

 Other websites 

 MITRE ATLAS: Adversarial Threat Landscape for Artificial-Intelligence Systems
 NIST 8269 Draft: A Taxonomy and Terminology of Adversarial Machine Learning
 NIPS 2007 Workshop on Machine Learning in Adversarial Environments for Computer Security
 AlfaSVMLib  – Adversarial Label Flip Attacks against Support Vector Machines

 Dagstuhl Perspectives Workshop on "Machine Learning Methods for Computer Security"
 Workshop on Artificial Intelligence and Security, (AISec) Series

Category:Machine learning
Category:Computer security
Category:AI safety