Can Machine Learning Ever Be Trustworthy?

I gave the Booz Allen Hamilton Distinguished Colloquium at the University of Maryland on Can Machine Learning Ever Be Trustworthy?. Video · SpeakerDeck Abstract Machine learning has produced extraordinary results over the past few years, and machine learning systems are rapidly being deployed for critical tasks, even in adversarial environments. This talk will survey some of the reasons building trustworthy machine learning systems is inherently impossible, and dive into some recent research on adversarial examples.

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Mutually Assured Destruction and the Impending AI Apocalypse

I gave a keynote talk at USENIX Workshop of Offensive Technologies, Baltimore, Maryland, 13 August 2018. The title and abstract are what I provided for the WOOT program, but unfortunately (or maybe fortunately for humanity!) I wasn’t able to actually figure out a talk to match the title and abstract I provided. The history of security includes a long series of arms races, where a new technology emerges and is subsequently developed and exploited by both defenders and attackers.

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

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.

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Lessons from the Last 3000 Years of Adversarial Examples

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]

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Feature Squeezing at NDSS

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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