Analyzing the noise robustness of deep neural networks

Cao, Kelei, Liu, Mengchen, Su, Hang, Wu, Jing ORCID: https://orcid.org/0000-0001-5123-9861, Zhu, Jun and Liu, Shixia 2021. Analyzing the noise robustness of deep neural networks. IEEE Transactions on Visualization and Computer Graphics 27 (7) , pp. 3289-3304. 10.1109/TVCG.2020.2969185

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Abstract

Adversarial examples, generated by adding small but intentionally imperceptible perturbations to normal examples, can mislead deep neural networks (DNNs) to make incorrect predictions. Although much work has been done on both adversarial attack and defense, a fine-grained understanding of adversarial examples is still lacking. To address this issue, we present a visual analysis method to explain why adversarial examples are misclassified. The key is to compare and analyze the datapaths of both the adversarial and normal examples. A datapath is a group of critical neurons along with their connections. We formulate the datapath extraction as a subset selection problem and solve it by constructing and training a neural network. A multi-level visualization consisting of a network-level visualization of data flows, a layer-level visualization of feature maps, and a neuron-level visualization of learned features, has been designed to help investigate how datapaths of adversarial and normal examples diverge and merge in the prediction process. A quantitative evaluation and a case study were conducted to demonstrate the promise of our method to explain the misclassification of adversarial examples.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISSN:	1077-2626
Date of First Compliant Deposit:	4 February 2020
Date of Acceptance:	20 January 2020
Last Modified:	23 Nov 2024 15:15
URI:	https://orca.cardiff.ac.uk/id/eprint/129315

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