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06/02/2023
Harnessing large-language models to generate private synthetic text
Differentially private (DP) training methods like DP-SGD can protect sen...
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02/18/2023
RetVec: Resilient and Efficient Text Vectorizer
This paper describes RetVec, a resilient multilingual embedding scheme d...
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12/28/2022
Publishing Efficient On-device Models Increases Adversarial Vulnerability
Recent increases in the computational demands of deep neural networks (D...
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11/24/2022
Differentially Private Image Classification from Features
Leveraging transfer learning has recently been shown to be an effective ...
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05/06/2022
Large Scale Transfer Learning for Differentially Private Image Classification
Differential Privacy (DP) provides a formal framework for training machi...
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06/08/2021
Handcrafted Backdoors in Deep Neural Networks
Deep neural networks (DNNs), while accurate, are expensive to train. Man...
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10/22/2020
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
Convex relaxations have emerged as a promising approach for verifying de...
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08/06/2018
Adversarial Vision Challenge
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitat...
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03/31/2018
Adversarial Attacks and Defences Competition
To accelerate research on adversarial examples and robustness of machine...
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03/16/2018
Adversarial Logit Pairing
In this paper, we develop improved techniques for defending against adve...
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05/19/2017
Ensemble Adversarial Training: Attacks and Defenses
Machine learning models are vulnerable to adversarial examples, inputs m...
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11/04/2016
Adversarial Machine Learning at Scale
Adversarial examples are malicious inputs designed to fool machine learn...
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10/03/2016
cleverhans v2.0.0: an adversarial machine learning library
cleverhans is a software library that provides standardized reference im...
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07/08/2016
