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arXiv · 2405.18627

PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics

Sunay Bhat·Jeffrey Jiang·Omead Pooladzandi·Alexander Branch·Gregory Pottie
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Abstract

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.

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Sunay Bhat, Jeffrey Jiang, Omead Pooladzandi, Alexander Branch, Gregory Pottie. 2024-05-28. PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics. https://arxiv.org/abs/2405.18627

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