Diffusion Denoising as a Certified Defense against Clean-label Poisoning
Sanghyun Hong Nicholas Carlini Alexey Kurakin
We present a certified defense to clean-label poisoning attacks. These
attacks work by injecting a small number of poisoning samples (e.g., 1%) that
contain $p$-norm bounded adversarial perturbations into the training data to
induce a targeted misclassification of a test-time input. Inspired by the
adversarial robustness achieved by $denoised$ $smoothing$, we show how an
off-the-shelf diffusion model can sanitize the tampered training data. We
extensively test our defense against seven clean-label poisoning attacks and
reduce their attack success to 0-16% with only a negligible drop in the test
time accuracy. We compare our defense with existing countermeasures against
clean-label poisoning, showing that the defense reduces the attack success the
most and offers the best model utility. Our results highlight the need for
future work on developing stronger clean-label attacks and using our certified
yet practical defense as a strong baseline to evaluate these attacks.