A developer’s guide to machine learning security
VentureBeat
Ben Dickson
Machine learning has become an important component of many applications we use today. And adding machine learning capabilities to applications is becoming increasingly easy. Many ML libraries and online services don’t even require a thorough knowledge of machine learning.
However, even easy-to-use machine learning systems come with their own challenges. Among them is the threat of adversarial attacks, which has become one of the important concerns of ML applications.
Adversarial attacks are different from other types of security threats that programmers are used to dealing with. Therefore, the first step to countering them is to understand the different types of adversarial attacks and the weak spots of the machine learning pipeline.
In this post, I will try to provide a zoomed-out view of...