  We propose an AID-purifier that can boost the robustness of
adversarially-trained networks by purifying their inputs. AID-purifier is an
auxiliary network that works as an add-on to an already trained main
classifier. To keep it computationally light, it is trained as a discriminator
with a binary cross-entropy loss. To obtain additionally useful information
from the adversarial examples, the architecture design is closely related to
information maximization principles where two layers of the main classification
network are piped to the auxiliary network. To assist the iterative
optimization procedure of purification, the auxiliary network is trained with
AVmixup. AID-purifier can be used together with other purifiers such as
PixelDefend for an extra enhancement. The overall results indicate that the
best performing adversarially-trained networks can be enhanced by the best
performing purification networks, where AID-purifier is a competitive candidate
that is light and robust.
