Presentation attacks such as using a contact lens with
a printed pattern or printouts of an iris can be utilized to
bypass a biometric security system. The first international
iris liveness competition was launched in 2013 in order
to assess the performance of presentation attack detection
(PAD) algorithms, with a second competition in 2015. This
paper presents results of the third competition, LivDet-Iris
2017. Three software-based approaches to Presentation Attack
Detection were submitted. Four datasets of live and
spoof images were tested with an additional cross-sensor
test. New datasets and novel situations of data have resulted
in this competition being of a higher difficulty than
previous competitions. Anonymous received the best results
with a rate of rejected live samples of 3.36% and rate of accepted
spoof samples of 14.71%. The results show that even
with advances, printed iris attacks as well as patterned contacts
lenses are still difficult for software-based systems to
detect. Printed iris images were easier to be differentiated
from live images in comparison to patterned contact lenses
as was also seen in previous competitions.
more »
« less
A Universal Anti-Spoofing Approach for Contactless Fingerprint Biometric Systems
With the increasing integration of smartphones into our daily lives, fingerphotos are becoming a potential contactless authentication method. While it offers convenience, it is also more vulnerable to spoofing using various presentation attack instruments (PAI). The contactless fingerprint is an emerging biometric authentication but has not yet been heavily investigated for anti-spoofing. While existing anti-spoofing approaches demonstrated fair results, they have encountered challenges in terms of universality and scalability to detect any unseen/unknown spoofed samples. To address this issue, we propose a universal presentation attack detection method for contactless fingerprints, despite having limited knowledge of presentation attack samples. We generated synthetic contactless fingerprints using StyleGAN from live finger photos and integrating them to train a semi-supervised ResNet-18 model. A novel joint loss function, combining the Arcface and Center loss, is introduced with a regularization to balance between the two loss functions and minimize the variations within the live samples while enhancing the inter-class variations between the deepfake and live samples. We also conducted a comprehensive comparison of different regularizations’ impact on the joint loss function for presentation attack detection (PAD) and explored the performance of a modified ResNet-18 architecture with different activation functions (i.e., leaky ReLU and RelU) in conjunction with Arcface and center loss. Finally, we evaluate the performance of the model using unseen types of spoof attacks and live data. Our proposed method achieves a Bona Fide Classification Error Rate (BPCER) of 0.12%, an Attack Presentation Classification Error Rate (APCER) of 0.63%, and an Average Classification Error Rate (ACER) of 0.37%.
more »
« less
- NSF-PAR ID:
- 10496400
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Int. Joint Conference on Biometrics (IJCB'23)
- ISBN:
- 979-8-3503-3726-6
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Location:
- Ljubljana, Slovenia
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
This paper describes the results of the 2023 edition of the “LivDet” series of iris presentation attack detection (PAD) competitions. New elements in this fifth competition include (1) GAN-generated iris images as a category of presentation attack instruments (PAI), and (2) an evaluation of human accuracy at detecting PAI as a reference benchmark. Clarkson University and the University of Notre Dame contributed image datasets for the competition, composed of samples representing seven different PAI categories, as well as baseline PAD algorithms. Fraunhofer IGD, Beijing University of Civil Engineering and Architecture, and Hochschule Darmstadt contributed results for a total of eight PAD algorithms to the competition. Accuracy results are analyzed by different PAI types, and compared to human accuracy. Overall, the Fraunhofer IGD algorithm, using an attention-based pixel-wise binary supervision network, showed the best-weighted accuracy results (average classification error rate of 37.31%), while the Beijing University of Civil Engineering and Architecture’s algorithm won when equal weights for each PAI were given (average classification rate of 22.15%). These results suggest that iris PAD is still a challenging problem.more » « less
-
Biometrics have been widely adopted for enhancing user authentication, benefiting usability by exploiting pervasive and collectible unique characteristics from physiological or behavioral traits of human. However, successful attacks on "static" biometrics such as fingerprints have been reported where an adversary acquires users' biometrics stealthily and compromises non-resilient biometrics. To mitigate the vulnerabilities of static biometrics, we leverage the unique and nonlinear hand-surface vibration response and design a system called Velody to defend against various attacks including replay and synthesis. The Velody system relies on two major properties in hand-surface vibration responses: uniqueness, contributed by physiological characteristics of human hands, and nonlinearity, whose complexity prevents attackers from predicting the response to an unseen challenge. Velody employs a challenge-response protocol. By changing the vibration challenge, the system elicits input-dependent nonlinear "symptoms" and unique spectrotemporal features in the vibration response, stopping both replay and synthesis attacks. Also, a large number of disposable challenge-response pairs can be collected during enrollment passively for daily authentication sessions. We build a prototype of Velody with an off-the-shelf vibration speaker and accelerometers to verify its usability and security through a comprehensive user experiment. Our results show that Velody demonstrates both strong security and long-term consistency with a low equal error rate (EER) of 5.8% against impersonation attack while correctly rejecting all other attacks including replay and synthesis attacks using a very short vibration challenge.more » « less
-
Traditional fingerprint authentication requires the acquisition of data through touch-based specialized sensors. However, due to many hygienic concerns including the global spread of the COVID virus through contact with a surface has led to an increased interest in contactless fingerprint image acquisition methods. Matching fingerprints acquired using contactless imaging against contact-based images brings up the problem of performing cross modal fingerprint matching for identity verification. In this paper, we propose a cost-effective, highly accurate and secure end-to-end contactless fingerprint recognition solution. The proposed framework first segments the finger region from an image scan of the hand using a mobile phone camera. For this purpose, we developed a cross-platform mobile application for fingerprint enrollment, verification, and authentication keeping security, robustness, and accessibility in mind. The segmented finger images go through fingerprint enhancement to highlight discriminative ridge-based features. A novel deep convolutional network is proposed to learn a representation from the enhanced images based on the optimization of various losses. The proposed algorithms for each stage are evaluated on multiple publicly available contactless databases. Our matching accuracy and the associated security employed in the system establishes the strength of the proposed solution framework.more » « less
-
Fingerprint capture systems can be fooled by widely accessible methods to spoof the system using fake fingers, known as presentation attacks. As biometric recognition systems become more extensively relied upon at international borders and in consumer electronics, presentation attacks are becoming an increasingly serious issue. A robust solution is needed that can handle the increased variability and complexity of spoofing techniques. This paper demonstrates the viability of utilizing a sensor with time-series and color-sensing capabilities to improve the robust-ness of a traditional fingerprint sensor and introduces a comprehensive fingerprint dataset with over 36,000 image sequences and a state-of-the-art set of spoofing techniques. The specific sensor used in this research captures a traditional gray-scale static capture and a time-series color capture simultaneously. Two different methods for Presentation Attack Detection (PAD) are used to assess the benefit of a color dynamic capture. The first algorithm utilizes Static-Temporal Feature Engineering on the fingerprint capture to generate a classification decision. The second generates its classification decision using features extracted by way of the Inception V3 CNN trained on ImageNet. Classification performance is evaluated using features extracted exclusively from the static capture, exclusively from the dynamic capture, and on a fusion of the two feature sets. With both PAD approaches we find that the fusion of the dynamic and static feature-set is shown to improve performance to a level not individually achievable.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IJCB57857.2023.10448939
