PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics
<|reference_start|>PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics: 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.<|reference_end|>
@article{bhat2024puregen:,
    title={PureGen: Universal Data Purification for Train-Time Poison Defense via
  Generative Model Dynamics},
    author={Sunay Bhat, Jeffrey Jiang, Omead Pooladzandi, Alexander Branch,
  Gregory Pottie},
    journal={arXiv preprint arXiv:2405.18627},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18627},
    primaryClass={cs.LG cs.AI cs.CR}
}