Varun Chandrasekaran

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University of Wisconsin-Madison

Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers

Detecting anomalous inputs, such as adversarial and out-of-distribution ...
07/29/2020 ∙ by Jayaram Raghuram, et al. ∙ 16 ∙ share

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Face-Off: Adversarial Face Obfuscation

Advances in deep learning have made face recognition increasingly feasib...
03/19/2020 ∙ by Chuhan Gao, et al. ∙ 0 ∙ share

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On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping

Machine learning algorithms are vulnerable to data poisoning attacks. Pr...
02/26/2020 ∙ by Sanghyun Hong, et al. ∙ 0 ∙ share

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Machine Unlearning

Once users have shared their data online, it is generally difficult for ...
12/09/2019 ∙ by Lucas Bourtoule, et al. ∙ 53 ∙ share

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Generating Semantic Adversarial Examples with Differentiable Rendering

Machine learning (ML) algorithms, especially deep neural networks, have ...
10/02/2019 ∙ by Lakshya Jain, et al. ∙ 23 ∙ share

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Enhancing ML Robustness Using Physical-World Constraints

Recent advances in Machine Learning (ML) have demonstrated that neural n...
05/26/2019 ∙ by Varun Chandrasekaran, et al. ∙ 0 ∙ share

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Characterizing Privacy Perceptions of Voice Assistants: A Technology Probe Study

The increasing pervasiveness of voice assistants in the home poses sever...
12/01/2018 ∙ by Varun Chandrasekaran, et al. ∙ 0 ∙ share

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Model Extraction and Active Learning

Machine learning is being increasingly used by individuals, research ins...
11/05/2018 ∙ by Varun Chandrasekaran, et al. ∙ 14 ∙ share

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Varun Chandrasekaran

Featured Co-authors

Kamalika Chaudhuri

Nicolas Papernot

Somesh Jha

David Lie

Tudor Dumitraş

Suman Banerjee

Xi Wu

Kassem Fawaz

Yiğitcan Kaya

Steven Chen

Sanghyun Hong

Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers

Face-Off: Adversarial Face Obfuscation

On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping

Machine Unlearning

Generating Semantic Adversarial Examples with Differentiable Rendering

Enhancing ML Robustness Using Physical-World Constraints

Characterizing Privacy Perceptions of Voice Assistants: A Technology Probe Study

Model Extraction and Active Learning