2 papers
cs.AI2025
PUREVQ-GAN: Defending Data Poisoning Attacks through Vector-Quantized Bottlenecks
Alexander Branch, Omead Pooladzandi, Radin Khosraviani +3
We introduce PureVQ-GAN, a defense against data poisoning that forces backdoor triggers through a discrete bottleneck using Vector-Quantized VAE with GAN discriminator. By quantizi…
cs.LG2024
PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics
Sunay Bhat, Jeffrey Jiang, Omead Pooladzandi +2
Train-time data poisoning attacks threaten machine learning models by introducing adversarial examples during training, leading to misclassification. Current defense methods often…