605 citations · 1.2k across the 17 of their papers we have counts for
19 papers
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning
Harrison Foley, Liam Fowl, Tom Goldstein +1
Data poisoning for reinforcement learning has historically focused on general performance degradation, and targeted attacks have been successful via perturbations that involve cont…
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach +1
Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. Despite their success, most of the current data-driven solutions lack proper quant…
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition
Valeriia Cherepanova, Micah Goldblum, Harrison Foley +4
Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike. These…
Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach +1
In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function ba…
Robust Optimization as Data Augmentation for Large-scale Graphs
Kezhi Kong, Guohao Li, Mucong Ding +5
Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the perfor…
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching
Jonas Geiping, Liam Fowl, W. Ronny Huang +4
Data Poisoning attacks modify training data to maliciously control a model trained on such data. In this work, we focus on targeted poisoning attacks which cause a reclassification…