Blackbox Interpretability: Next Frontier in Adversarial ML Evasion


Posted on in Presentations

Blackbox interpretability is an emerging field of study in AI and ML. ML often incorporates millions of features, making model decision interpretation nearly impossible. Interpretability promises answers to those questions, but the bad news is that attackers can also use it to identify weak spots in your defenses. Learn how you can use it to identify when attackers have discovered your secret sauce.

Learning Objectives:
1: Understand blackbox interpretability.
2: See how attackers can leverage blackbox interpretability to evade detection (feature evasion).
3: See how defenders can leverage blackbox interpretability to identify feature evasion.

Access This and Other RSAC Conference Presentations with Your Free RSAC Membership

Your RSAC Membership also includes AI-powered summaries, mind maps, and slides for Conference presentations, Group Discussions with experts, and more.

Watch Now >>
Participants
Greg Ellison

Participant

Data Scientist, Microsoft

Holly Stewart

Participant

Principal Research Manager, Microsoft


Share With Your Community