Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA

Use the Jupyter Notebook to integrate adversarial attacks into a model training pipeline to detect vulnerabilities

Animesh Singh (IBM), Tommy Li (IBM)
1:50pm2:30pm Thursday, March 28, 2019
Secondary topics:  Security and Privacy
Average rating: ****.
(4.50, 2 ratings)

Who is this presentation for?

  • Software developers

Level

Intermediate

Prerequisite knowledge

  • A basic understanding of machine learning

What you'll learn

  • Understand the importance of model robustness and how to apply it to existing models

Description

With great power comes great responsibility. Adversarial examples in AI pose an asymmetrical challenge with respect to attackers and defenders. AI developers must be empowered to defend deep neural networks against adversarial attacks and allow rapid crafting and analysis of attack and defense methods for machine learning models.

Animesh Singh and Tommy Li explain how to implement state-of-the-art methods for attacking and defending classifiers using the open source Adversarial Robustness Toolbox. The library provides AI developers with interfaces that support the composition of comprehensive defense systems using individual methods as building blocks. Animesh and Tommy then demonstrate how to use a Jupyter notebook to leverage attack methods from the Adversarial Robustness Toolbox (ART) into a model training pipeline on Fabric for Deep Learning (FfDL). This notebook trains a CNN model on the Fashion MNIST dataset, and the generated adversarial samples are used to evaluate the robustness of the trained model. 

Photo of Animesh Singh

Animesh Singh

IBM

Animesh Singh is a senior technical staff member (STSM) and program director for the IBM Watson and Cloud Platform, where he leads machine learning and deep learning initiatives on IBM Cloud and works with communities and customers to design and implement deep learning, machine learning, and cloud computing frameworks. He has a proven track record of driving design and implementation of private and public cloud solutions from concept to production. Animesh has worked on cutting-edge projects for IBM enterprise customers in the telco, banking, and healthcare industries, particularly focusing on cloud and virtualization technologies, and led the design and development first IBM public cloud offering.

Photo of Tommy Li

Tommy Li

IBM

Tommy Li is a software developer at IBM focusing on cloud, container, and infrastructure technology. He’s worked on various developer journeys that provide use cases on cloud-computing solutions, such as Kubernetes, microservices, and hybrid cloud deployments. He’s passionate about machine learning and big data.