Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models

Post by Hannah Chen. Our work on balanced adversarial training looks at how to train models that are robust to two different types of adversarial examples: Hannah Chen, Yangfeng Ji, David Evans. Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models. In The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), Abu Dhabi, 7-11 December 2022. [ArXiv] Adversarial Examples At the broadest level, an adversarial example is an input crafted intentionally to confuse a model.

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Best Submission Award at VISxAI 2022

Poisoning Attacks and Subpopulation Susceptibility by Evan Rose, Fnu Suya, and David Evans won the Best Submission Award at the 5th Workshop on Visualization for AI Explainability. Undergraduate student Evan Rose led the work and presented it at VISxAI in Oklahoma City, 17 October 2022. Congratulations to #VISxAI's Best Submission Awards: šŸ† K-Means Clustering: An Explorable Explainer by @yizhe_ang https://t.co/BULW33WPzo šŸ† Poisoning Attacks and Subpopulation Susceptibility by Evan Rose, @suyafnu, and @UdacityDave https://t.

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Visualizing Poisoning

How does a poisoning attack work and why are some groups more susceptible to being victimized by a poisoning attack? We’ve posted work that helps understand how poisoning attacks work with some engaging visualizations: Poisoning Attacks and Subpopulation Susceptibility An Experimental Exploration on the Effectiveness of Poisoning Attacks Evan Rose, Fnu Suya, and David Evans Follow the link to try the interactive version! Machine learning is susceptible to poisoning attacks in which adversaries inject maliciously crafted training data into the training set to induce specific model behavior.

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Congratulations, Dr. Zhang

Congratulations to Xiao Zhang for successfully defending his PhD thesis! Dr. Zhang and his PhD committee: Somesh Jha (University of Wisconsin), David Evans, Tom Fletcher; Tianxi Li (UVA Statistics), David Wu (UT Austin), Mohammad Mahmoody; Xiao Zhang. Xiao will join the CISPA Helmholtz Center for Information Security in SaarbrĆ¼cken, Germany this fall as a tenure-track faculty member. From Characterizing Intrinsic Robustness to Adversarially Robust Machine Learning The prevalence of adversarial examples raises questions about the reliability of machine learning systems, especially for their deployment in critical applications.

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BIML: What Machine Learnt Models Reveal

I gave a talk in the Berryville Institute of Machine Learning in the Barn series on What Machine Learnt Models Reveal, which is now available as an edited video: David Evans, a professor of computer science researching security and privacy at the University of Virginia, talks about data leakage risk in ML systems and different approaches used to attack and secure models and datasets. Juxtaposing adversarial risks that target records and those aimed at attributes, David shows that differential privacy cannot capture all inference risks, and calls for more research based on privacy experiments aimed at both datasets and distributions.

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ICLR 2022: Understanding Intrinsic Robustness Using Label Uncertainty

(Blog post written by Xiao Zhang) Motivated by the empirical hardness of developing robust classifiers against adversarial perturbations, researchers began asking the question ā€œDoes there even exist a robust classifier?ā€. This is formulated as the intrinsic robustness problem (Mahloujifar et al., 2019), where the goal is to characterize the maximum adversarial robustness possible for a given robust classification problem. Building upon the connection between adversarial robustness and classifierā€™s error region, it has been shown that if we restrict the search to the set of imperfect classifiers, the intrinsic robustness problem can be reduced to the concentration of measure problem.

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Microsoft Research Summit: Surprising (and unsurprising) Inference Risks in Machine Learning

Here are the slides for my talk at the Practical and Theoretical Privacy of Machine Learning Training Pipelines Workshop at the Microsoft Research Summit (21 October 2021): Surprising (and Unsurprising) Inference Risks in Machine Learning [PDF] The work by Bargav Jayaraman (with Katherine Knipmeyer, Lingxiao Wang, and Quanquan Gu) that I talked about on improving membership inference attacks is described in more details here: Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, David Evans.

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UVA News Article

UVA News has an article by Audra Book on our research on security and privacy of machine learning (with some very nice quotes from several students in the group, and me saying something positive about the NSA!): Computer science professor David Evans and his team conduct experiments to understand security and privacy risks associated with machine learning, 8 September 2021. David Evans, professor of computer science in the University of Virginia School of Engineering and Applied Science, is leading research to understand how machine learning models can be compromised.

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Model-Targeted Poisoning Attacks with Provable Convergence

(Post by Sean Miller, using images adapted from Suya’s talk slides) Data Poisoning Attacks Machine learning models are often trained using data from untrusted sources, leaving them open to poisoning attacks where adversaries use their control over a small fraction of that training data to poison the model in a particular way. Most work on poisoning attacks is directly driven by an attacker’s objective, where the adversary chooses poisoning points that maximize some target objective.

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On the Risks of Distribution Inference

(Cross-post by Anshuman Suri) Inference attacks seek to infer sensitive information about the training process of a revealed machine-learned model, most often about the training data. Standard inference attacks (which we call ā€œdataset inference attacksā€) aim to learn something about a particular record that may have been in that training data. For example, in a membership inference attack (Reza Shokri et al., Membership Inference Attacks Against Machine Learning Models, IEEE S&P 2017), the adversary aims to infer whether or not a particular record was included in the training data.

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