ICLR 2019: Cost-Sensitive Robustness against Adversarial Examples

Xiao Zhang and my paper on Cost-Sensitive Robustness against Adversarial Examples has been accepted to ICLR 2019. Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. However, these methods assume that all the adversarial transformations provide equal value for adversaries, which is seldom the case in real-world applications. We advocate for cost-sensitive robustness as the criteria for measuring the classifier’s performance for specific tasks.

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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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Center for Trustworthy Machine Learning

The National Science Foundation announced the Center for Trustworthy Machine Learning today, a new five-year SaTC Frontier Center “to develop a rigorous understanding of the security risks of the use of machine learning and to devise the tools, metrics and methods to manage and mitigate security vulnerabilities.” The Center is lead by Patrick McDaniel at Penn State University, and in addition to our group, includes Dan Boneh and Percy Liang (Stanford University), Kamalika Chaudhuri (University of California San Diego), Somesh Jha (University of Wisconsin) and Dawn Song (University of California Berkeley).

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Artificial intelligence: the new ghost in the machine

Engineering and Technology Magazine (a publication of the British Institution of Engineering and Technology has an article that highlights adversarial machine learning research: Artificial intelligence: the new ghost in the machine, 10 October 2018, by Chris Edwards. Although researchers such as David Evans of the University of Virginia see a full explanation being a little way off in the future, the massive number of parameters encoded by DNNs and the avoidance of overtraining due to SGD may have an answer to why the networks can hallucinate images and, as a result, see things that are not there and ignore those that are.

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USENIX Security 2018

Three SRG posters were presented at USENIX Security Symposium 2018 in Baltimore, Maryland: Nathaniel Grevatt (GDPR-Compliant Data Processing: Improving Pseudonymization with Multi-Party Computation) Matthew Wallace and Parvesh Samayamanthula (Deceiving Privacy Policy Classifiers with Adversarial Examples) Guy Verrier (How is GDPR Affecting Privacy Policies?, joint with Haonan Chen and Yuan Tian) There were also a surprising number of appearances by an unidentified unicorn:

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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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Dependable and Secure Machine Learning

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?

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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