Adversarial Machine Learning | Security Research Group

Dependable and Secure Machine Learning

7 July 2018 adversarial machine learning, dependable machine learning, Homa Alemzadeh

I co-organized, with Homa Alemzadeh and Karthik Pattabiraman, a workshop on trustworthy machine learning attached to DSN 2018, in Luxembourg: DSML: Dependable and Secure Machine Learning.

DLS Keynote: Is 'adversarial examples' an Adversarial Example?

29 May 2018 adversarial machine learning, talks, videos

I gave a keynote talk at the 1st Deep Learning and Security Workshop (co-located with the 39th IEEE Symposium on Security and Privacy). San Francisco, California. 24 May 2018

Abstract

Over the past few years, there has been an explosion of research in security of machine learning and on adversarial examples in particular. Although this is in many ways a new and immature research area, the general problem of adversarial examples has been a core problem in information security for thousands of years. In this talk, I’ll look at some of the long-forgotten lessons from that quest and attempt to understand what, if anything, has changed now we are in the era of deep learning classifiers. I will survey the prevailing definitions for “adversarial examples”, argue that those definitions are unlikely to be the right ones, and raise questions about whether those definitions are leading us astray.

Lessons from the Last 3000 Years of Adversarial Examples

15 May 2018 adversarial machine learning, China, Huawei, Battista Biggio

I spoke on Lessons from the Last 3000 Years of Adversarial Examples at Huawei’s Strategy and Technology Workshop in Shenzhen, China, 15 May 2018.

We also got to tour Huawei’s new research and development campus, under construction about 40 minutes from Shenzhen. It is pretty close to Disneyland, with its own railroad and villages themed after different European cities (Paris, Bologna, etc.).

Huawei’s New Research and Development Campus [More Pictures]

Feature Squeezing at NDSS

25 February 2018 adversarial machine learning, feature squeezing, Weilin Xu

Weilin Xu presented Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks at the Network and Distributed System Security Symposium 2018. San Diego, CA. 21 February 2018.

Paper: Weilin Xu, David Evans, Yanjun Qi. Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks. NDSS 2018. [PDF]

Project Site: EvadeML.org

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