@inproceedings{
pooladzandi2024puregen,
title={PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics},
author={Omead Pooladzandi and Sunay Gajanan Bhat and Jeffrey Jiang and Alexander Branch and Gregory Pottie},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ZeihWodDVh}
}
Train-time data poisoning attacks threaten machine learning models by introducing adversarial examples during training, leading to misclassification. Current defense methods often reduce generalization performance, are attack-specific, and impose significant training overhead. To address this, we introduce a set of universal data purification methods using a stochastic transform, $\Psi(x)$, realized via iterative Langevin dynamics of Energy-Based Models (EBMs), Denoising Diffusion Probabilistic Models (DDPMs), or both. These approaches purify poisoned data with minimal impact on classifier generalization. Our specially trained EBMs and DDPMs provide state-of-the-art defense against various attacks (including Narcissus, Bullseye Polytope, Gradient Matching) on CIFAR-10, Tiny-ImageNet, and CINIC-10, without needing attack or classifier-specific information. We discuss performance trade-offs and show that our methods remain highly effective even with poisoned or distributionally shifted generative model training data.
PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics
Omead Pooladzandi Sunay Gajanan Bhat Jeffrey Jiang Alexander Branch Gregory Pottie