scholarly journals Fingerprint Presentation Attack Detection Utilizing Spatio-Temporal Features

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2059
Author(s):  
Anas Husseis ◽  
Judith Liu-Jimenez ◽  
Raul Sanchez-Reillo
Keyword(s):  
Classification Scheme ◽  
Temporal Variations ◽  
Attack Detection ◽  
Polynomial Kernel ◽  
Degree Polynomial ◽  
Temporal Features ◽  
Bona Fide ◽  
Spatio Temporal ◽  
Dynamic Presentation ◽  
Presentation Attack Detection

This paper presents a novel mechanism for fingerprint dynamic presentation attack detection. We utilize five spatio-temporal feature extractors to efficiently eliminate and mitigate different presentation attack species. The feature extractors are selected such that the fingerprint ridge/valley pattern is consolidated with the temporal variations within the pattern in fingerprint videos. An SVM classification scheme, with a second degree polynomial kernel, is used in our presentation attack detection subsystem to classify bona fide and attack presentations. The experiment protocol and evaluation are conducted following the ISO/IEC 30107-3:2017 standard. Our proposed approach demonstrates efficient capability of detecting presentation attacks with significantly low BPCER where BPCER is 1.11% for an optical sensor and 3.89% for a thermal sensor at 5% APCER for both.

scholarly journals The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme

2021 ◽  
Vol 11 (17) ◽  
pp. 7883
Author(s):  
Anas Husseis ◽  
Judith Liu-Jimenez ◽  
Raul Sanchez-Reillo
Keyword(s):  
Error Rate ◽  
Attack Detection ◽  
Fingerprint Recognition ◽  
Classification Error ◽  
Final Decision ◽  
Classification Error Rate ◽  
Detection Scheme ◽  
Bona Fide ◽  
The Impact ◽  
Presentation Attack Detection

Fingerprint recognition systems have been widely deployed in authentication and verification applications, ranging from personal smartphones to border control systems. Recently, the biometric society has raised concerns about presentation attacks that aim to manipulate the biometric system’s final decision by presenting artificial fingerprint traits to the sensor. In this paper, we propose a presentation attack detection scheme that exploits the natural fingerprint phenomena, and analyzes the dynamic variation of a fingerprint’s impression when the user applies additional pressure during the presentation. For that purpose, we collected a novel dynamic dataset with an instructed acquisition scenario. Two sensing technologies are used in the data collection, thermal and optical. Additionally, we collected attack presentations using seven presentation attack instrument species considering the same acquisition circumstances. The proposed mechanism is evaluated following the directives of the standard ISO/IEC 30107. The comparison between ordinary and pressure presentations shows higher accuracy and generalizability for the latter. The proposed approach demonstrates efficient capability of detecting presentation attacks with low bona fide presentation classification error rate (BPCER) where BPCER is 0% for an optical sensor and 1.66% for a thermal sensor at 5% attack presentation classification error rate (APCER) for both.

scholarly journals On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5686
Author(s):  
Jascha Kolberg ◽  
Daniel Gläsner ◽  
Ralph Breithaupt ◽  
Marta Gomez-Barrero ◽  
Jörg Reinhold ◽  
...  
Keyword(s):  
Thin Film ◽  
Human Skin ◽  
Thin Film Transistor ◽  
Attack Detection ◽  
Biometric Recognition ◽  
The Public ◽  
Biometric Systems ◽  
Bona Fide ◽  
User Friendly ◽  
Presentation Attack Detection

Within the last few decades, the need for subject authentication has grown steadily, and biometric recognition technology has been established as a reliable alternative to passwords and tokens, offering automatic decisions. However, as unsupervised processes, biometric systems are vulnerable to presentation attacks targeting the capture devices, where presentation attack instruments (PAI) instead of bona fide characteristics are presented. Due to the capture devices being exposed to the public, any person could potentially execute such attacks. In this work, a fingerprint capture device based on thin film transistor (TFT) technology has been modified to additionally acquire the impedances of the presented fingers. Since the conductance of human skin differs from artificial PAIs, those impedance values were used to train a presentation attack detection (PAD) algorithm. Based on a dataset comprising 42 different PAI species, the results showed remarkable performance in detecting most attack presentations with an APCER = 2.89% in a user-friendly scenario specified by a BPCER = 0.2%. However, additional experiments utilising unknown attacks revealed a weakness towards particular PAI species.

scholarly journals Intrusion Detection Model Using Temporal Convolutional Network Blend Into Attention Mechanism

2022 ◽  
Vol 16 (1) ◽  
pp. 1-20
Author(s):  
Ping Zhao ◽  
Zhijie Fan* ◽  
Zhiwei Cao ◽  
Xin Li
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Spatial Information ◽  
Attack Detection ◽  
Attention Mechanism ◽  
Surveillance Network ◽  
Convolutional Network ◽  
Detection Model ◽  
Temporal Features ◽  
Spatio Temporal

In order to improve the ability to detect network attacks, traditional intrusion detection models often used convolutional neural networks to encode spatial information or recurrent neural networks to obtain temporal features of the data. Some models combined the two methods to extract spatio-temporal features. However, these approaches used separate models and learned features insufficiently. This paper presented an improved model based on temporal convolutional networks (TCN) and attention mechanism. The causal and dilation convolution can capture the spatio-temporal dependencies of the data. The residual blocks allow the network to transfer information in a cross-layered manner, enabling in-depth network learning. Meanwhile, attention mechanism can enhance the model's attention to the relevant anomalous features of different attacks. Finally, this paper compared models results on the KDD CUP99 and UNSW-NB15 datasets. Besides, the authors apply the model to video surveillance network attack detection scenarios. The result shows that the model has advantages in evaluation metrics.

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