Video is All You Need

Li, Lin; Chen, Chao; Pan, Lei; Zhang, Jun; Xiang, Yang

doi:10.1145/3560830.3563722

Cited by 15 publications

(2 citation statements)

References 31 publications

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“…One example is the use of stealthy recording of PPG signals from non-genuine measurement sites (also known as presentation attacks) to deceive PPG-based authentication systems [12]. Another approach is to remotely steal PPG signals from video recordings of the victim, known as remote PPG (rPPG), which can be used to breach the authentication system [13].…”

Section: Introductionmentioning

confidence: 99%

PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG Signals

Aly¹,

Pietro²

2023

Preprint

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<p>In this paper, we propose a novel session-based continuous authentication scheme using photoplethysmography (PPG). The supporting model is developed in two phases: first, we utilize deep autoencoders to extract features from PPG signals, and later we adopt Local Outlier Factor (LOF) to authenticate the user. LOF is trained only on legitimate user data, making it practical for real-world scenarios. In detail, the model is trained on session-based data and generates user signatures at the beginning of each session with a short buffer duration. Despite its simplicity, our solution achieves staggering performance: an F1 score of 91.3% and 91.0% on the CapnoBase and BIMDC benchmarking datasets, respectively. Compared to a state-of-the-art baseline model, the proposed solution reduces by 2.3% the EER on the BIMDC dataset while attaining a 6%+ increase in the F1 metric with respect to the CapnoBase dataset. Additionally, the proposed solution has a faster runtime, requiring approximately 0.5 seconds to process 18,000 beats compared to the competing model's 3.6 seconds, resulting in an 85% reduction in runtime.</p>

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Section: Introductionmentioning

confidence: 99%

PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG Signals

Aly¹,

Pietro²

2023

Preprint

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG Signals

Aly,

Di Pietro

2023

Preprint

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<p>In this paper, we propose a novel session-based continuous authentication model using photoplethysmography (PPG). Unlike previous PPG-based authentication techniques that generate user signatures only during the initial interaction, our session-based approach tackles inter session PPG drifting by generating a user signature at the start of each session. Our model is composed by two modules: Firstly, heavy deep autoencoders (AE) are utilized for feature extraction and, secondly, a lightweight Local Outlier Factor (LOF) is employed for user authentication. Additionally, we introduce a continuous updating system for the LOF model, which automatically recovers from security breaches and can enhance authentication accuracy by more than 9%. Our experiments show that in a single-session scenario, our model achieves authentication accuracies of 93.5% and 91.8% on the CapnoBase and BIMDC benchmarking datasets, respectively, outperforming the state-of-the-art baseline model by 3.2% and 1.6% on both datasets, respectively. In multiple-session scenarios, our scheme attains an authentication accuracy of 95% when tested on the BioSec2 dataset, effectively mitigating inter-session PPG drifting and achieving an advantage of more than 8.5% in authentication accuracy over the state-of-the-art method. In terms of execution speed, our solution is seven times faster at runtime compared to competing state-of-the-art solutions.</p>

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