Adaptive Adversarial Logits Pairing
This work addresses adversarial robustness for image classification systems, presenting an incremental improvement over existing methods.
The paper tackled the problem of adversarial robustness in image classification by identifying limitations in Adversarial Logits Pairing (ALP) and proposing Adaptive Adversarial Logits Pairing (AALP), which improved defense performance on multiple datasets through adaptive feature optimization and sample weighting.
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.