On the Generalisation Capabilities of Fingerprint Presentation Attack
Detection Methods in the Short Wave Infrared Domain
- URL: http://arxiv.org/abs/2010.09566v2
- Date: Mon, 11 Jan 2021 16:45:36 GMT
- Title: On the Generalisation Capabilities of Fingerprint Presentation Attack
Detection Methods in the Short Wave Infrared Domain
- Authors: Jascha Kolberg and Marta Gomez-Barrero and Christoph Busch
- Abstract summary: Presentation attack detection methods are of utmost importance in order to distinguish between bona fide and attack presentations.
We evaluate the generalisability of multiple PAD algorithms on a dataset of 19,711 bona fide and 4,339 PA samples, including 45 different PAI species.
- Score: 13.351759885287526
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Nowadays, fingerprint-based biometric recognition systems are becoming
increasingly popular. However, in spite of their numerous advantages, biometric
capture devices are usually exposed to the public and thus vulnerable to
presentation attacks (PAs). Therefore, presentation attack detection (PAD)
methods are of utmost importance in order to distinguish between bona fide and
attack presentations. Due to the nearly unlimited possibilities to create new
presentation attack instruments (PAIs), unknown attacks are a threat to
existing PAD algorithms. This fact motivates research on generalisation
capabilities in order to find PAD methods that are resilient to new attacks. In
this context, we evaluate the generalisability of multiple PAD algorithms on a
dataset of 19,711 bona fide and 4,339 PA samples, including 45 different PAI
species. The PAD data is captured in the short wave infrared domain and the
results discuss the advantages and drawbacks of this PAD technique regarding
unknown attacks.
Related papers
- Unsupervised Fingerphoto Presentation Attack Detection With Diffusion Models [8.979820109339286]
Smartphone-based contactless fingerphoto authentication has become a reliable alternative to traditional contact-based fingerprint biometric systems.
Despite its convenience, fingerprint authentication through fingerphotos is more vulnerable to presentation attacks.
We propose a novel unsupervised approach based on a state-of-the-art deep-learning-based diffusion model, the Denoising Probabilistic Diffusion Model (DDPM)
The proposed approach detects Presentation Attacks (PA) by calculating the reconstruction similarity between the input and output pairs of the DDPM.
arXiv Detail & Related papers (2024-09-27T11:07:48Z) - AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning [93.77763753231338]
Adversarial Contrastive Prompt Tuning (ACPT) is proposed to fine-tune the CLIP image encoder to extract similar embeddings for any two intermediate adversarial queries.
We show that ACPT can detect 7 state-of-the-art query-based attacks with $>99%$ detection rate within 5 shots.
We also show that ACPT is robust to 3 types of adaptive attacks.
arXiv Detail & Related papers (2024-08-04T09:53:50Z) - PRAT: PRofiling Adversarial aTtacks [52.693011665938734]
We introduce a novel problem of PRofiling Adversarial aTtacks (PRAT)
Given an adversarial example, the objective of PRAT is to identify the attack used to generate it.
We use AID to devise a novel framework for the PRAT objective.
arXiv Detail & Related papers (2023-09-20T07:42:51Z) - On the Universal Adversarial Perturbations for Efficient Data-free
Adversarial Detection [55.73320979733527]
We propose a data-agnostic adversarial detection framework, which induces different responses between normal and adversarial samples to UAPs.
Experimental results show that our method achieves competitive detection performance on various text classification tasks.
arXiv Detail & Related papers (2023-06-27T02:54:07Z) - A Review of Adversarial Attack and Defense for Classification Methods [78.50824774203495]
This paper focuses on the generation and guarding of adversarial examples.
It is the hope of the authors that this paper will encourage more statisticians to work on this important and exciting field of generating and defending against adversarial examples.
arXiv Detail & Related papers (2021-11-18T22:13:43Z) - Taming Self-Supervised Learning for Presentation Attack Detection:
De-Folding and De-Mixing [42.733666815035534]
Biometric systems are vulnerable to Presentation Attacks performed using various Presentation Attack Instruments (PAIs)
We propose a self-supervised learning-based method, denoted as DF-DM.
DF-DM is based on a global-local view coupled with De-Folding and De-Mixing to derive the task-specific representation for PAD.
arXiv Detail & Related papers (2021-09-09T08:38:17Z) - Towards Adversarial Patch Analysis and Certified Defense against Crowd
Counting [61.99564267735242]
Crowd counting has drawn much attention due to its importance in safety-critical surveillance systems.
Recent studies have demonstrated that deep neural network (DNN) methods are vulnerable to adversarial attacks.
We propose a robust attack strategy called Adversarial Patch Attack with Momentum to evaluate the robustness of crowd counting models.
arXiv Detail & Related papers (2021-04-22T05:10:55Z) - On the Generalisation Capabilities of Fisher Vector based Face
Presentation Attack Detection [13.93832810177247]
Face Presentation Attack Detection techniques have reported a good detection performance when they are evaluated on known Presentation Attack Instruments.
In this work, we use a new feature space based on Fisher Vectors, computed from compact Binarised Statistical Image Features histograms.
This new representation, evaluated for challenging unknown attacks taken from freely available facial databases, shows promising results.
arXiv Detail & Related papers (2021-03-02T13:49:06Z) - Anomaly Detection with Convolutional Autoencoders for Fingerprint
Presentation Attack Detection [11.879849130630406]
Presentation attack detection (PAD) methods are used to determine whether samples stem from a bona fide subject or from a presentation attack instrument (PAI)
We propose a new PAD technique based on autoencoders (AEs) trained only on bona fide samples (i.e. one-class) captured in the short wave infrared domain.
arXiv Detail & Related papers (2020-08-18T15:33:41Z) - Anomaly Detection-Based Unknown Face Presentation Attack Detection [74.4918294453537]
Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection.
In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection.
The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task.
arXiv Detail & Related papers (2020-07-11T21:20:55Z) - A Survey on Unknown Presentation Attack Detection for Fingerprint [4.424609902825527]
Biometrics researchers have developed Presentation Attack Detection (PAD) methods as a countermeasure to presentation attacks (PA)
PAD is usually done by training a machine learning classifier for known attacks for a given dataset, and they achieve high accuracy.
We present a comprehensive survey on existing PAD algorithms for fingerprint recognition systems, specifically from the standpoint of detecting unknown PAD.
arXiv Detail & Related papers (2020-05-17T18:46:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.