Is Robust Machine Learning Possible?

Machine learning has shown remarkable success in solving complex classification problems, but current machine learning techniques produce models that are vulnerable to adversaries who may wish to confuse them, especially when used for security applications like malware classification.

The key assumption of machine learning is that a model that is trained on training data will perform well in deployment because the training data is representative of the data that will be seen when the classifier is deployed.

When machine learning classifiers are used in security applications, however, adversaries may be able to generate samples that exploit the invalidity of this assumption.

Our project is focused on understanding, evaluating, and improving the effectiveness of machine learning methods in the presence of motivated and sophisticated adversaries.

Projects

Empirically Measuring Concentration
Method to empirically measure concentration of real datasets, finding that it does not explain the lack of robustness of state-of-the-art models.

Feature Squeezing
Reduce search space for adversaries by coalescing inputs. (Top row shows $\ell_0$ adversarial examples, squeezed by median smoothing.)
Cost-Senstivie Robustness
Genetic Search
Evolutionary framework to automatically find variants that preserve malicious behavior but evade a target classifier.

Papers

Fnu Suya, Jianfeng Chi, David Evans, and Yuan Tian. Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries. In 29th USENIX Security Symposium. Boston, MA. August 12–14, 2020. [PDF] [arXiV] [Code]

Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody, and David Evans. Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness. In NeurIPS 2019. Vancouver, December 2019. (Earlier versions appeared in Debugging Machine Learning Models and Safe Machine Learning: Specification, Robustness and Assurance, workshops attached to Seventh International Conference on Learning Representations (ICLR). New Orleans. May 2019. [PDF] [arXiv] [Post] [Code]

Xiao Zhang and David Evans. Cost-Sensitive Robustness against Adversarial Examples. In Seventh International Conference on Learning Representations (ICLR). New Orleans. May 2019. [arXiv] [OpenReview] [PDF]

Weilin Xu, David Evans, Yanjun Qi. Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks. 2018 Network and Distributed System Security Symposium. 18-21 February, San Diego, California. Full paper (15 pages): [PDF]

Weilin Xu, Yanjun Qi, and David Evans. Automatically Evading Classifiers A Case Study on PDF Malware Classifiers. Network and Distributed Systems Symposium 2016, 21-24 February 2016, San Diego, California. Full paper (15 pages): [PDF]

More Papers…

Talks

Trustworthy Machine Learning. Mini-course at 19th International School on Foundations of Security Analysis and Design. Bertinoro, Italy. 26–28 August 2019.

Can Machine Learning Ever Be Trustworthy?. University of Maryland, Booz Allen Hamilton Distinguished Colloquium. 7 December 2018. [SpeakerDeck] [Video]

Mutually Assured Destruction and the Impending AI Apocalypse. Opening keynote, 12th USENIX Workshop on Offensive Technologies 2018. (Co-located with USENIX Security Symposium.) Baltimore, Maryland. 13 August 2018. [SpeakerDeck]

Is “Adversarial Examples” an Adversarial Example. Keynote talk at 1st Deep Learning and Security Workshop (co-located with the 39th IEEE Symposium on Security and Privacy). San Francisco, California. 24 May 2018. [SpeakerDeck]


More Talks…

Code

Hybrid Batch Attacks: https://github.com/suyeecav/Hybrid-Attack

Empirically Measuring Concentration: https://github.com/xiaozhanguva/Measure-Concentration

EvadeML-Zoo: https://github.com/mzweilin/EvadeML-Zoo

Genetic Evasion: https://github.com/uvasrg/EvadeML (Weilin Xu)

Cost-Sensitive Robustness: https://github.com/xiaozhanguva/Cost-Sensitive-Robustness (Xiao Zhang)

Adversarial Learning Playground: https://github.com/QData/AdversarialDNN-Playground (Andrew Norton) (mostly supersceded by the EvadeML-Zoo toolkit)

Feature Squeezing: https://github.com/uvasrg/FeatureSqueezing (Weilin Xu) (supersceded by the EvadeML-Zoo toolkit)

Team

Hannah Chen (PhD student, working on adversarial natural language processing)
Mainuddin Ahmad Jonas (PhD student, working on adversarial examples)
Fnu Suya (PhD student, working on batch attacks)
Xiao Zhang (PhD student, working on cost-sensitive adversarial robustness)

David Evans (Faculty Co-Advisor)
Yanjun Qi (Faculty Co-Advisor for Weilin Xu)
Yuan Tian (Faculty Co-Advisor for Fnu Suya)

Alumni

Weilin Xu (PhD Student who initiated project, lead work on Feature Squeezing and Genetic Evasion, now at Intel Research, Oregon)

Johannes Johnson (Undergraduate researcher working on malware classification and evasion, summer 2018)
Anant Kharkar (Undergraduate Researcher worked on Genetic Evasion, 2016-2018)
Noah Kim (Undergraduate Researcher worked on EvadeML-Zoo, 2017)
Yuancheng Lin (Undergraduate researchers working on adversarial examples, summer 2018) Felix Park (Undergradaute Researcher, worked on color-aware preprocessors, 2017-2018)
Helen Simecek (Undergraduate researcher working on Genetic Evasion, 2017-2019)
Matthew Wallace (Undergraduate researcher working on natural language deception, 2018-2019; now at University of Wisconsin)