Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness

Emanuele Ballarin; Alessio Ansuini; Luca Bortolussi

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arXiv · 2306.06081

Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness

Emanuele Ballarin·Alessio Ansuini·Luca Bortolussi

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Abstract

In this work, we propose a novel adversarial defence mechanism for image classification - CARSO - blending the paradigms of adversarial training and adversarial purification in a synergistic robustness-enhancing way. The method builds upon an adversarially-trained classifier, and learns to map its internal representation associated with a potentially perturbed input onto a distribution of tentative clean reconstructions. Multiple samples from such distribution are classified by the same adversarially-trained model, and a carefully chosen aggregation of its outputs finally constitutes the robust prediction of interest. Experimental evaluation by a well-established benchmark of strong adaptive attacks, across different image datasets, shows that CARSO is able to defend itself against adaptive end-to-end white-box attacks devised for stochastic defences. Paying a modest clean accuracy toll, our method improves by a significant margin the state-of-the-art for Cifar-10, Cifar-100, and TinyImageNet-200 $\ell_\infty$ robust classification accuracy against AutoAttack. Code, and instructions to obtain pre-trained models are available at: https://github.com/emaballarin/CARSO .

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BibTeX RIS

Emanuele Ballarin, Alessio Ansuini, Luca Bortolussi. 2023-05-25. Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness. https://arxiv.org/abs/2306.06081

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