What developers should know about machine learning security
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 my latest column, I provide a zoomed-out view of the adversarial attack and defense landscape. Hopefully, this can help programmers and product managers who don’t have a technical background in machine learning get a better grasp of how they can spot threats and protect their ML-powered applications.
Read the full article on TechTalks.
For more on adversarial attacks: