  We introduce a feature scattering-based adversarial training approach for
improving model robustness against adversarial attacks. Conventional
adversarial training approaches leverage a supervised scheme (either targeted
or non-targeted) in generating attacks for training, which typically suffer
from issues such as label leaking as noted in recent works. Differently, the
proposed approach generates adversarial images for training through feature
scattering in the latent space, which is unsupervised in nature and avoids
label leaking. More importantly, this new approach generates perturbed images
in a collaborative fashion, taking the inter-sample relationships into
consideration. We conduct analysis on model robustness and demonstrate the
effectiveness of the proposed approach through extensively experiments on
different datasets compared with state-of-the-art approaches.
