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We propose an AID-purifier that can boost the robustness ofadversarially-trained networks by purifying their inputs. AID-purifier is anauxiliary network that works as an add-on to an already trained mainclassifier. To keep it computationally light, it is trained as a discriminatorwith a binary cross-entropy loss. To obtain additionally useful informationfrom the adversarial examples, the architecture design is closely related toinformation maximization principles where two layers of the main classificationnetwork are piped to the auxiliary network. To assist the iterativeoptimization procedure of purification, the auxiliary network is trained withAVmixup. AID-purifier can be used together with other purifiers such asPixelDefend for an extra enhancement. The overall results indicate that thebest performing adversarially-trained networks can be enhanced by the bestperforming purification networks, where AID-purifier is a competitive candidatethat is light and robust.

AID-Purifier: A Light Auxiliary Network for Boosting Adversarial Defense