Related papers: Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection

Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection

URL: http://arxiv.org/abs/2012.03483v1
Date: Mon, 7 Dec 2020 07:21:18 GMT
Title: Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection
Authors: Byunggill Joe, Jihun Hamm, Sung Ju Hwang, Sooel Son, Insik Shin
Abstract summary: We propose a new probabilistic adversarial detector motivated by a recently introduced non-robust feature. In this paper, we consider the non-robust features as a common property of adversarial examples, and we deduce it is possible to find a cluster in representation space corresponding to the property. This idea leads us to probability estimate distribution of adversarial representations in a separate cluster, and leverage the distribution for a likelihood based adversarial detector.
Score: 50.03939695025513
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Although deep neural networks have shown promising performances on various tasks, they are susceptible to incorrect predictions induced by imperceptibly small perturbations in inputs. A large number of previous works proposed to detect adversarial attacks. Yet, most of them cannot effectively detect them against adaptive whitebox attacks where an adversary has the knowledge of the model and the defense method. In this paper, we propose a new probabilistic adversarial detector motivated by a recently introduced non-robust feature. We consider the non-robust features as a common property of adversarial examples, and we deduce it is possible to find a cluster in representation space corresponding to the property. This idea leads us to probability estimate distribution of adversarial representations in a separate cluster, and leverage the distribution for a likelihood based adversarial detector.

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