An Implicit Identity Authentication Method Based on Deep Connected Attention CNN for Wild Environment

Yang, Wenqing; Wang, Meilin; Zou, Shihong; Peng, Junhao; Xu, Guosheng

doi:10.1145/3456415.3457222

Cited by 1 publication

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Touch Dynamics Deep learning algorithms have been used to make classification decisions for many touch-based mobile authentication models. For studies [7][8][9], CNN architectures were used for classification. In [7], researchers used their proposed feature representation tactic, multiple channels biological graph (MCBG), with CNN to enhance their continuous mobile authentication scheme.…”

Section: Literature Reviewmentioning

confidence: 99%

“…When compared with other classification algorithms, CNN had the best authentication accuracy with each feature graph input. Researchers of [8] evaluated implicit touch authentication with multidimensional touch and motion sensor data. For their classifier, CNN was used for its high performance with continuous implicit authentication (CIA) schemes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Often, these data are difficult to process for authentication. While some studies such as [7,8,35,65] account for this variability in their data collection and preprocessing stages and allow unconstrained data collection and realistic smartphone usage, many studies instruct their volunteers to follow more rigid instructions for data collection and testing. This can result in models that are not equipped to handle authentication in scenarios.…”

Section: Limitationsmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning and Machine Learning, Better Together Than Apart: A Review on Biometrics Mobile Authentication

Kokal

Vanamala

Dave

2023

JCP

View full text Add to dashboard Cite

Throughout the past several decades, mobile devices have evolved in capability and popularity at growing rates while improvement in security has fallen behind. As smartphones now hold mass quantities of sensitive information from millions of people around the world, addressing this gap in security is crucial. Recently, researchers have experimented with behavioral and physiological biometrics-based authentication to improve mobile device security. Continuing the previous work in this field, this study identifies popular dynamics in behavioral and physiological smartphone authentication and aims to provide a comprehensive review of their performance with various deep learning and machine learning algorithms. We found that utilizing hybrid schemes with deep learning features and deep learning/machine learning classification can improve authentication performance. Throughout this paper, the benefits, limitations, and recommendations for future work will be discussed.

show abstract