491 citations · 632 across the 24 of their papers we have counts for
9 papers · 1 filter
Class-Proportional Coreset Selection for Difficulty-Separable Data
Elisa Tsai, Haizhong Zheng, Atul Prakash
High-quality training data is essential for building reliable and efficient machine learning systems. One-shot coreset selection addresses this by pruning the dataset while maintai…
What Really is a Member? Discrediting Membership Inference via Poisoning
Neal Mangaokar, Ashish Hooda, Zhuohang Li +5
Membership inference tests aim to determine whether a particular data point was included in a language model's training set. However, recent works have shown that such tests often…
Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks
Ashish Hooda, Neal Mangaokar, Ryan Feng +3
Adversarial examples threaten the integrity of machine learning systems with alarming success rates even under constrained black-box conditions. Stateful defenses have emerged as a…
Coverage-centric Coreset Selection for High Pruning Rates
Haizhong Zheng, Rui Liu, Fan Lai +1
One-shot coreset selection aims to select a representative subset of the training data, given a pruning rate, that can later be used to train future models while retaining high acc…
Defending Object Detectors against Patch Attacks with Out-of-Distribution Smoothing
Ryan Feng, Neal Mangaokar, Jihye Choi +2
Patch attacks against object detectors have been of recent interest due to their being physically realizable and more closely aligned with practical systems. In response to this th…
Concept-based Explanations for Out-Of-Distribution Detectors
Jihye Choi, Jayaram Raghuram, Ryan Feng +3
Out-of-distribution (OOD) detection plays a crucial role in ensuring the safe deployment of deep neural network (DNN) classifiers. While a myriad of methods have focused on improvi…
D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles
Ashish Hooda, Neal Mangaokar, Ryan Feng +3
Detecting diffusion-generated deepfake images remains an open problem. Current detection methods fail against an adversary who adds imperceptible adversarial perturbations to the d…
Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples
Nelson Manohar-Alers, Ryan Feng, Sahib Singh +2
We present DeClaW, a system for detecting, classifying, and warning of adversarial inputs presented to a classification neural network. In contrast to current state-of-the-art meth…
Robust Classification using Robust Feature Augmentation
Kevin Eykholt, Swati Gupta, Atul Prakash +3
Existing deep neural networks, say for image classification, have been shown to be vulnerable to adversarial images that can cause a DNN misclassification, without any perceptible…