Provably Safeguarding a Classifier from OOD and Adversarial Samples
Nicolas Atienza, Johanne Cohen, Christophe Labreuche, Michele Sebag
Abstract
This paper aims to transform a trained classifier into an abstaining classifier, such that the latter is provably protected from out-of-distribution and adversarial samples. The proposed Sample-efficient Probabilistic Detection using Extreme Value Theory (SPADE) approach relies on a Generalized Extreme Value (GEV) model of the training distribution in the latent space of the classifier. Under mild assumptions, this GEV model allows for formally characterizing out-of-distribution and adversarial samples and rejecting them. Empirical validation of the approach is conducted on various neural architectures (ResNet, VGG, and Vision Transformer) and considers medium and large-sized datasets (CIFAR-10, CIFAR-100, and ImageNet). The results show the stability and frugality of the GEV model and demonstrate SPADE’s efficiency compared to the state-of-the-art methods.
BibTeX
@inproceedings{
atienza2025provably,
title={Provably Safeguarding a Classifier from {OOD} and Adversarial Samples},
author={Nicolas Atienza and Johanne Cohen and Christophe Labreuche and Michele Sebag},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=kwCHcaeHrf}
}