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Machine learning has demonstrated remarkable performance over finitedatasets, yet whether the scores over the fixed benchmarks can sufficientlyindicate the model's performance in the real world is still in discussion. Inreality, an ideal robust model will probably behave similarly to the oracle(e.g., the human users), thus a good evaluation protocol is probably toevaluate the models' behaviors in comparison to the oracle. In this paper, weintroduce a new robustness measurement that directly measures the imageclassification model's performance compared with a surrogate oracle (i.e., afoundation model). Besides, we design a simple method that can accomplish theevaluation beyond the scope of the benchmarks. Our method extends the imagedatasets with new samples that are sufficiently perturbed to be distinct fromthe ones in the original sets, but are still bounded within the sameimage-label structure the original test image represents, constrained by afoundation model pretrained with a large amount of samples. As a result, ournew method will offer us a new way to evaluate the models' robustnessperformance, free of limitations of fixed benchmarks or constrainedperturbations, although scoped by the power of the oracle. In addition to theevaluation results, we also leverage our generated data to understand thebehaviors of the model and our new evaluation strategies.

Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models