Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication
Publication Date
1-1-2022
Document Type
Contribution to a Book
Publication Title
Advances in Information Security
Volume
54
DOI
10.1007/978-3-030-97087-1_10
First Page
243
Last Page
259
Abstract
Gesture-based authentication has emerged as a non-intrusive, effective means of authenticating users on mobile devices. Typically, such authentication techniques have relied on classical machine learning techniques, but recently, deep learning techniques have been applied this problem. Although prior research has shown that deep learning models are vulnerable to adversarial attacks, relatively little research has been done in the adversarial domain for behavioral biometrics. In this research, we collect tri-axial accelerometer gesture data (TAGD) from 46 users and perform classification experiments with both classical machine learning and deep learning models. Specifically, we train and test support vector machines (SVM) and convolutional neural networks (CNN). We then consider a realistic adversarial attack, where we assume the attacker has access to real users’ TAGD data, but not the authentication model. We use a deep convolutional generative adversarial network (DC-GAN) to create adversarial samples, and we show that our deep learning model is surprisingly robust to such an attack scenario.
Department
Computer Science
Recommended Citation
Elliu Huang, Fabio Di Troia, and Mark Stamp. "Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication" Advances in Information Security (2022): 243-259. https://doi.org/10.1007/978-3-030-97087-1_10
