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Found 3 papers, 2 papers with code
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability
Alternatively, model ensemble adversarial attacks are proposed to fuse outputs from surrogate models with diverse architectures to get an ensemble loss, making the generated adversarial example more likely to transfer to other models as it can fool multiple models concurrently.
SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation
In this paper, we present a new meta self-training pipeline, named SRoUDA, for improving adversarial robustness of UDA models.
Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustness
However, most of the existing adversarial training methods focus on improving the robust accuracy by strengthening the adversarial examples but neglecting the increasing shift between natural data and adversarial examples, leading to a dramatic decrease in natural accuracy.
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