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Imperceptible poisoning attacks on entire datasets have recently been toutedas methods for protecting data privacy. However, among a number of defensespreventing the practical use of these techniques, early-stopping stands out asa simple, yet effective defense. To gauge poisons' vulnerability toearly-stopping, we benchmark error-minimizing, error-maximizing, and syntheticpoisons in terms of peak test accuracy over 100 epochs and make a number ofsurprising observations. First, we find that poisons that reach a low trainingloss faster have lower peak test accuracy. Second, we find that a currentstate-of-the-art error-maximizing poison is 7 times less effective when poisontraining is stopped at epoch 8. Third, we find that stronger, more transferableadversarial attacks do not make stronger poisons. We advocate for evaluatingpoisons in terms of peak test accuracy.

Poisons that are learned faster are more effective