What Doesn't Kill You Makes You Robust(er): How to Adversarially Train against Data Poisoning

Jonas Geiping, Liam H Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, Tom Goldstein

Keywords: Data Poisoning, Poisoning Defenses, Adversarial Training, Empirical Defenses, Robustness, Security

Abstract:

Data poisoning is a threat model in which a malicious actor tampers with training data to manipulate outcomes at inference time. A variety of defenses against this threat model have been proposed, but each suffers from at least one of the following flaws: they are easily overcome by adaptive attacks, they severely reduce testing performance, or they cannot generalize to diverse data poisoning threat models. Adversarial training, and its variants, are currently considered the only empirically strong defense against (inference-time) adversarial attacks. In this work, we extend the adversarial training framework to defend against (training-time) data poisoning. Our method desensitizes networks to the effects of such attacks by creating poisons during training and injecting them into training batches.
We show that this defense withstands adaptive attacks, generalizes to diverse threat models, and incurs a better performance trade-off than previous defenses.

One-sentence Summary: Robust training is a good defense against targeted data poisoning, even for strong, adaptive attacks.