Visit the Pennsylvania State University Home Page

Center for Trustworthy Machine Learning

  • Home
    • Background
    • Our Research
  • Outreach & Education
  • People
    • Investigators
    • Industrial Advisory Board
    • Graduate Students
    • Undergraduate Students
  • Publications
  • Data Sets

Our Research

Research

The Center for Trustworthy Machine Learning is focused on three interconnected and parallel thrusts that represent the different approaches to attacking ML systems: training attacks, inference attacks, and abuse.

  • The first thrust explores inference time security, namely methods to defend a trained model from adversarial inputs. This effort focuses on developing formally grounded measurements of robustness against adversarial examples (defenses), as well as algorithms for their generation (attacks).
  • The second thrust explores robustness during training time. Here, the main goal is to develop rigorously grounded measures of robustness to attacks that corrupt the training data. This is achieved through the development of new training techniques that are robust to such forms of manipulation.
  • The third thrust explores the general security implications of sophisticated ML algorithms. The Center PIs explore the general implications of generative ML models, such as models that generate (fake) content or data, and develop ways to distinguish such content from real content.  This is achieved through the exploration of mechanisms to prevent the theft of a machine learning model by an adversary who interacts with the model.

The Center is also constructing and distributing an extensive evaluation platform that will let the Center PIs and other investigators experiment with new attacks and defenses on a variety of data sets, to test their effectiveness.

Details of the center portal with regards to upcoming tools and open source code will be announced later.

Thrust 1 - Transformations

Thrust 1 - Theory

Thrust 1 - Practice

Thrust 2 - Crafting Adversarial Examples

Thrust 2 - Class Boundary issues.

Thrust 2 - Fake content generation (Obama)

CleverHans

CodaLab

 Visit the Pennsylvania State University Home Page
Copyright 2025 © The Pennsylvania State University Privacy Non-Discrimination Equal Opportunity Accessibility Legal

Support for the Center for Trustworthy Machine Learning (CTML) is provided through NSF Grant #(CNS-1805310), part of the NSF Secure and Trustworthy Cyberspace Program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Additional support is provided byPenn State University,Stanford University,UC Berkeley,UC San Diego,University of Wisconsin,andUniversity of Virginia.