  It has been recognized that the data generated by the denoising diffusion
probabilistic model (DDPM) improves adversarial training. After two years of
rapid development in diffusion models, a question naturally arises: can better
diffusion models further improve adversarial training? This paper gives an
affirmative answer by employing the most recent diffusion model which has
higher efficiency ($\sim 20$ sampling steps) and image quality (lower FID
score) compared with DDPM. Our adversarially trained models achieve
state-of-the-art performance on RobustBench using only generated data (no
external datasets). Under the $\ell_\infty$-norm threat model with
$\epsilon=8/255$, our models achieve $70.69\%$ and $42.67\%$ robust accuracy on
CIFAR-10 and CIFAR-100, respectively, i.e. improving upon previous
state-of-the-art models by $+4.58\%$ and $+8.03\%$. Under the $\ell_2$-norm
threat model with $\epsilon=128/255$, our models achieve $84.86\%$ on CIFAR-10
($+4.44\%$). These results also beat previous works that use external data. We
also provide compelling results on the SVHN and TinyImageNet datasets. Our code
is available at https://github.com/wzekai99/DM-Improves-AT.
