  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.
