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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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06/01/2023
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception
Perception is crucial in the realm of autonomous driving systems, where ...
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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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10/28/2022
Coverage-centric Coreset Selection for High Pruning Rates
One-shot coreset selection aims to select a subset of the training data,...
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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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05/08/2020
Towards Robustness against Unsuspicious Adversarial Examples
Despite the remarkable success of deep neural networks, significant conc...
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05/06/2020
MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation
Model Stealing (MS) attacks allow an adversary with black-box access to ...
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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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12/27/2019
Efficient Adversarial Training with Transferable Adversarial Examples
Adversarial training is an effective defense method to protect classific...
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11/27/2019
Can Attention Masks Improve Adversarial Robustness?
Deep Neural Networks (DNNs) are known to be susceptible to adversarial e...
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09/12/2019
Transferable Adversarial Robustness using Adversarially Trained Autoencoders
Machine learning has proven to be an extremely useful tool for solving c...
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05/27/2019
Analyzing the Interpretability Robustness of Self-Explaining Models
Recently, interpretable models called self-explaining models (SEMs) have...
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05/26/2019
Robust Classification using Robust Feature Augmentation
Existing deep neural networks, say for image classification, have been s...
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01/29/2019
Beware of the App! On the Vulnerability Surface of Smart Devices through their Companion Apps
Internet of Things (IoT) devices are becoming increasingly important. Th...
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12/17/2018
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering
We provide a methodology, resilient feature engineering, for creating ad...
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10/31/2018
Securing IoT Apps with Fine-grained Control of Information Flows
Internet of Things is growing rapidly, with many connected devices now a...
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07/20/2018
Physical Adversarial Examples for Object Detectors
Deep neural networks (DNNs) are vulnerable to adversarial examples-malic...
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01/14/2018
Tyche: Risk-Based Permissions for Smart Home Platforms
Emerging smart home platforms, which interface with a variety of physica...
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12/21/2017
Note on Attacking Object Detectors with Adversarial Stickers
Deep learning has proven to be a powerful tool for computer vision and h...
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01/27/2014