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07/30/2023
Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks
Adversarial examples threaten the integrity of machine learning systems ...
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03/11/2023
Investigating Stateful Defenses Against Black-Box Adversarial Examples
Defending machine-learning (ML) models against white-box adversarial att...
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05/18/2022
Constraining the Attack Space of Machine Learning Models with Distribution Clamping Preprocessing
Preprocessing and outlier detection techniques have both been applied to...
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03/04/2022
Concept-based Explanations for Out-Of-Distribution Detectors
Out-of-distribution (OOD) detection plays a crucial role in ensuring the...
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02/11/2022
Towards Adversarially Robust Deepfake Detection: An Ensemble Approach
Detecting deepfakes is an important problem, but recent work has shown t...
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07/01/2021
Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples
We present DeClaW, a system for detecting, classifying, and warning of a...
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12/03/2020
Essential Features: Reducing the Attack Surface of Adversarial Perturbations with Robust Content-Aware Image Preprocessing
Adversaries are capable of adding perturbations to an image to fool mach...
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02/17/2020
Query-Efficient Physical Hard-Label Attacks on Deep Learning Visual Classification
We present Survival-OPT, a physical adversarial example algorithm in the...
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06/05/2019
