Direct attacks using fake images in iris verification
- URL: http://arxiv.org/abs/2111.00178v1
- Date: Sat, 30 Oct 2021 05:01:06 GMT
- Title: Direct attacks using fake images in iris verification
- Authors: Virginia Ruiz-Albacete, Pedro Tome-Gonzalez, Fernando
Alonso-Fernandez, Javier Galbally, Julian Fierrez, Javier Ortega-Garcia
- Abstract summary: A database of fake iris images has been created from real iris of the BioSec baseline database.
We show that the system is vulnerable to direct attacks, pointing out the importance of having countermeasures.
- Score: 59.68607707427014
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this contribution, the vulnerabilities of iris-based recognition systems
to direct attacks are studied. A database of fake iris images has been created
from real iris of the BioSec baseline database. Iris images are printed using a
commercial printer and then, presented at the iris sensor. We use for our
experiments a publicly available iris recognition system, which some
modifications to improve the iris segmentation step. Based on results achieved
on different operational scenarios, we show that the system is vulnerable to
direct attacks, pointing out the importance of having countermeasures against
this type of fraudulent actions.
Related papers
- Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis [11.5164036021499]
We present a review of state-of-the-art GAN-based synthetic iris image generation techniques.
We first survey the various methods that have been used for synthetic iris generation and specifically consider generators based on StyleGAN, RaSGAN, CIT-GAN, iWarpGAN, StarGAN, etc.
arXiv Detail & Related papers (2024-04-26T01:45:58Z) - Swap It Like Its Hot: Segmentation-based spoof attacks on eye-tracking images [1.4732811715354455]
Biometric authentication is susceptible to spoofing through physical or digital manipulation.
Liveness detection classifies gaze data as real or fake, which is sufficient to detect physical presentation attacks.
We propose IrisSwap as a novel attack on gaze-based liveness detection.
arXiv Detail & Related papers (2024-04-22T01:59:48Z) - EyePreserve: Identity-Preserving Iris Synthesis [8.973296574093506]
This paper presents the first method of fully data-driven, identity-preserving, pupil size-varying synthesis of iris images.
Two immediate applications of the proposed approach are: (a) synthesis of, or enhancement of the existing biometric datasets for iris recognition, and (b) helping forensic human experts in examining iris image pairs with significant differences in pupil dilation.
arXiv Detail & Related papers (2023-12-19T10:29:29Z) - Periocular biometrics: databases, algorithms and directions [69.35569554213679]
Periocular biometrics has been established as an independent modality due to concerns on the performance of iris or face systems in uncontrolled conditions.
This paper presents a review of the state of the art in periocular biometric research.
arXiv Detail & Related papers (2023-07-26T11:14:36Z) - Iris super-resolution using CNNs: is photo-realism important to iris
recognition? [67.42500312968455]
Single image super-resolution techniques are emerging, especially with the use of convolutional neural networks (CNNs)
In this work, the authors explore single image super-resolution using CNNs for iris recognition.
They validate their approach on a database of 1.872 near infrared iris images and on a mobile phone image database.
arXiv Detail & Related papers (2022-10-24T11:19:18Z) - Super-Resolution and Image Re-projection for Iris Recognition [67.42500312968455]
Convolutional Neural Networks (CNNs) using different deep learning approaches attempt to recover realistic texture and fine grained details from low resolution images.
In this work we explore the viability of these approaches for iris Super-Resolution (SR) in an iris recognition environment.
Results show that CNNs and image re-projection can improve the results specially for the accuracy of recognition systems.
arXiv Detail & Related papers (2022-10-20T09:46:23Z) - Super-Resolution for Selfie Biometrics: Introduction and Application to
Face and Iris [67.74999528342273]
Lack of resolution has a negative impact on the performance of image-based biometrics.
Super-resolution techniques have to be adapted for the particularities of images from a specific biometric modality.
This chapter presents an overview of recent advances in super-resolution reconstruction of face and iris images.
arXiv Detail & Related papers (2022-04-12T10:28:31Z) - Toward Accurate and Reliable Iris Segmentation Using Uncertainty
Learning [96.72850130126294]
We propose an Iris U-transformer (IrisUsformer) for accurate and reliable iris segmentation.
For better accuracy, we elaborately design IrisUsformer by adopting position-sensitive operation and re-packaging transformer block.
We show that IrisUsformer achieves better segmentation accuracy using 35% MACs of the SOTA IrisParseNet.
arXiv Detail & Related papers (2021-10-20T01:37:19Z) - Segmentation-Aware and Adaptive Iris Recognition [24.125681602124477]
The quality of iris images acquired at-a-distance or under less constrained imaging environments is known to degrade the iris matching accuracy.
The periocular information is inherently embedded in such iris images and can be exploited to assist in the iris recognition under such non-ideal scenarios.
This paper presents such a segmentation-assisted adaptive framework for more accurate less-constrained iris recognition.
arXiv Detail & Related papers (2019-12-31T04:31:37Z)
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.