  Adversarial examples provide an opportunity as well as impose a challenge for
understanding image classification systems. Based on the analysis of the
adversarial training solution Adversarial Logits Pairing (ALP), we observed in
this work that: (1) The inference of adversarially robust model tends to rely
on fewer high-contribution features compared with vulnerable ones. (2) The
training target of ALP doesn't fit well to a noticeable part of samples, where
the logits pairing loss is overemphasized and obstructs minimizing the
classification loss. Motivated by these observations, we design an Adaptive
Adversarial Logits Pairing (AALP) solution by modifying the training process
and training target of ALP. Specifically, AALP consists of an adaptive feature
optimization module with Guided Dropout to systematically pursue fewer
high-contribution features, and an adaptive sample weighting module by setting
sample-specific training weights to balance between logits pairing loss and
classification loss. The proposed AALP solution demonstrates superior defense
performance on multiple datasets with extensive experiments.
