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Robust Yet Efficient Conformal Prediction Sets
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Conformal prediction (CP) can convert any model's output into prediction sets guaranteed to include the true label with any user-specified probability. However, same as the model itself, CP is vulnerable to adversarial test examples (evasion) and perturbed calibration data (poisoning). We derive provably robust sets by bounding the worst-case change in conformity scores. Our tighter bounds lead to more efficient sets. We cover both continuous and discrete (sparse) data and our guarantees work both for evasion and poisoning attacks (on both features and labels).
Forward citations
Cited by 2 Pith papers
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Membership Inference Attacks with False Discovery Rate Control
A post-hoc wrapper, MIAFdR, converts any membership inference attack scores into conformal p-values and applies a Benjamini-Hochberg correction, guaranteeing that the expected proportion of non-members among flagged m...
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Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability
An adversarial attack and defense that respectively enlarge and shrink conformal prediction sets, with experiments on CIFAR-10, CIFAR-100 and mini-ImageNet.
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