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02/21/2023
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker
Finding classifiers robust to adversarial examples is critical for their...
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02/03/2023
Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning
The proliferation of global censorship has led to the development of a p...
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06/21/2022
Natural Backdoor Datasets
Extensive literature on backdoor poison attacks has studied attacks and ...
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06/20/2022
Understanding Robust Learning through the Lens of Representation Similarities
Representation learning, i.e. the generation of representations useful f...
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06/08/2022
Can Backdoor Attacks Survive Time-Varying Models?
Backdoors are powerful attacks against deep neural networks (DNNs). By p...
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10/13/2021
Traceback of Data Poisoning Attacks in Neural Networks
In adversarial machine learning, new defenses against attacks on deep le...
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09/07/2021
LEAF: Navigating Concept Drift in Cellular Networks
Operational networks commonly rely on machine learning models for many t...
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04/16/2021
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries
Understanding the fundamental limits of robust supervised learning has e...
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02/08/2021
A Real-time Defense against Website Fingerprinting Attacks
Anonymity systems like Tor are vulnerable to Website Fingerprinting (WF)...
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07/08/2020
A Critical Evaluation of Open-World Machine Learning
Open-world machine learning (ML) combines closed-world models trained on...
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05/17/2020
PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields
Localized adversarial patches aim to induce misclassification in machine...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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09/26/2019
Lower Bounds on Adversarial Robustness from Optimal Transport
While progress has been made in understanding the robustness of machine ...
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05/05/2019
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples
A large body of recent work has investigated the phenomenon of evasion a...
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11/29/2018
Analyzing Federated Learning through an Adversarial Lens
Federated learning distributes model training among a multitude of agent...
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06/05/2018
PAC-learning in the presence of evasion adversaries
The existence of evasion attacks during the test phase of machine learni...
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02/18/2018
DARTS: Deceiving Autonomous Cars with Toxic Signs
Sign recognition is an integral part of autonomous cars. Any misclassifi...
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01/09/2018
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos
We propose a new real-world attack against the computer vision based sys...
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12/27/2017