Deep Learning-Based Implicit Continuous Authentication of Smartphone User

Jose, Christy James; Rajasree, M. S.

doi:10.1007/978-981-16-8862-1_25

Cited by 2 publications

(4 citation statements)

References 17 publications

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“…In response, [16] introduced an open-set authentication (OSA) strategy that allows for zero-shot inference, where the model can predict unseen data. Moreover, in [16], in a realization that the exploration of various deep learning architectures for mobile biometrics authentication has been limited to LSTM networks [6,7], Convolutional LSTM [8,14], Autoencoders [15], and Temporal Convolutional Networks (TCN) [9], the authors attempt their solution with Transformers [22,23]. Furthermore, an implicit-ensembled model named PIEformer is proposed, noted on the ensembled model's capability of exhibiting robustness in open set scenarios, mainly due to their enhanced generalization performance [39].…”

Section: Related Workmentioning

confidence: 99%

“…Behavioral biometrics facilitates a seamless and smooth authentication process in this regard. This body of literature involves keystrokes [4], gait features [5], and touchstroke dynamics [6]. However, behavioral biometrics inherently includes various characteristics within a single identity, varying on factors such as mood.…”

Section: Introductionmentioning

confidence: 99%

“…However, behavioral biometrics inherently includes various characteristics within a single identity, varying on factors such as mood. Therefore, the machine learning approach has been widely adopted, especially for touchstroke dynamics [6][7][8][9]. For clarity, we note that touchstroke dynamics are coupled with gait features, whereas, in this work, we refer to touchstroke-gait feature multimodal signals as touchstrokes for brevity.…”

Section: Introductionmentioning

confidence: 99%

“…The touchstroke authentication problems can be framed as traditional binary classification (closed-set) [6][7][8][10][11][12][13][14], anomaly detection (closed-set) [7,15], or open-set authentication [16] problems. Among the most primitive is traditional binary classification, where access to an abundant amounts of genuine and impostor data is assumed, and the model is trained as a binary classifier upon such data.…”

Section: Introductionmentioning

confidence: 99%

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CoreTemp: Coreset Sampled Templates for Multimodal Mobile Biometrics

Yoon,

Park,

Kim

et al. 2024

Applied Sciences

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Smart devices have become the core ingredient in maintaining human society, where their applications span basic telecommunication, entertainment, education, and even critical security tasks. However, smartphone security measures have not kept pace with their ubiquitousness and convenience, exposing users to potential security breaches. Shading light on shortcomings of traditional security measures such as PINs gives rise to biometrics-based security measures. Open-set authentication with pretrained Transformers especially shows competitive performance in this context. Bringing this closer to practice, we propose CoreTemp, a greedy coreset sampled template, which offers substantially faster authentication speeds. In parallel with CoreTemp, we design a fast match algorithm where the combination shows robust performance in open-set mobile biometrics authentication. Designed to resemble the effects of ensembles with marginal increment in computation, we propose PIEformer+, where its application with CoreTemp has state-of-the-art performance. Benefiting from much more efficient authentication speeds to the best of our knowledge, we are the first to attempt identification in this context. Our proposed methodology achieves state-of-the-art results on HMOG and BBMAS datasets, particularly with much lower computational costs. In summary, this research introduces a novel integration of greedy coreset sampling with an advanced form of pretrained, implicitly ensembled Transformers (PIEformer+), greatly enhancing the speed and efficiency of mobile biometrics authentication, and also enabling identification, which sets a new benchmark in the relevant field.

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