MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples

• URL: http://arxiv.org/abs/2210.01111v1
• Date: Mon, 3 Oct 2022 17:50:57 GMT
• Title: MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples
• Authors: Jinyuan Jia and Wenjie Qu and Neil Zhenqiang Gong
• Abstract summary: MultiGuard is the first provably robust defense against adversarial examples to multi-label classification. Our major theoretical contribution is that we show a certain number of ground truth labels of an input are provably in the set of labels predicted by our MultiGuard.
• Score: 67.0982378001551
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-label classification, which predicts a set of labels for an input, has many applications. However, multiple recent studies showed that multi-label classification is vulnerable to adversarial examples. In particular, an attacker can manipulate the labels predicted by a multi-label classifier for an input via adding carefully crafted, human-imperceptible perturbation to it. Existing provable defenses for multi-class classification achieve sub-optimal provable robustness guarantees when generalized to multi-label classification. In this work, we propose MultiGuard, the first provably robust defense against adversarial examples to multi-label classification. Our MultiGuard leverages randomized smoothing, which is the state-of-the-art technique to build provably robust classifiers. Specifically, given an arbitrary multi-label classifier, our MultiGuard builds a smoothed multi-label classifier via adding random noise to the input. We consider isotropic Gaussian noise in this work. Our major theoretical contribution is that we show a certain number of ground truth labels of an input are provably in the set of labels predicted by our MultiGuard when the $\ell_2$-norm of the adversarial perturbation added to the input is bounded. Moreover, we design an algorithm to compute our provable robustness guarantees. Empirically, we evaluate our MultiGuard on VOC 2007, MS-COCO, and NUS-WIDE benchmark datasets. Our code is available at: \url{https://github.com/quwenjie/MultiGuard}

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