Wasserstein Robust Classification Error: Surrogate Loss Misalignment and Distributional Attacks

The paper investigates Wasserstein distributionally robust optimization (WDRO) for classification, showing that ordering hypotheses by WDRO risk aligns with robust accuracy only for a specific class of loss functions. It demonstrates that common losses like cross‑entropy can misalign robust accuracy and WDRO risk, and introduces Wasserstein distributional attacks (WDA) with a support‑search algorithm to directly evaluate robust accuracy. Experiments on CIFAR‑10, CIFAR‑100, and ImageNet reveal that WDA achieves the lowest adversarial accuracy in most settings.

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