Biometric authentication systems are crucial for security, but developing them involves various complexities, including privacy, security, and achieving high accuracy without directly storing pure biometric data in storage. We introduce an innovative image distortion technique that makes facial images unrecognizable to the eye but still identifiable by any custom embedding neural network model. Using the proposed approach, we test the reliability of biometric recognition networks by determining the maximum image distortion that does not change the predicted identity. Through experiments on MNIST and LFW datasets, we assess its effectiveness and compare it based on the traditional comparison metrics.
With increasing concerns about security, the need for highly secure physical biometrics-based authentication systems utilizing \emph{cancelable biometric} technologies is on the rise. Because the problem of cancelable template generation deals with the trade-off between template security and matching performance, many state-of-the-art algorithms successful in generating high quality cancelable biometrics all have random projection as one of their early processing steps. This paper therefore presents a formal analysis of why random projections is an essential step in cancelable biometrics. By f…
Cancelable Biometric Template Generation Using Random Feature Vector Transformations
Indian Institute of Information Technology and Management-Kerala（印度信息技术与管理学院（喀拉拉邦））；Cochin University of Science and Technology（科钦科学技术大学）；Kerala University of Digital Sciences, Innovation and Technology（喀拉拉邦数字科学、创新与技术大学）；Singapore Institute of Technology（新加坡理工学院）
Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition systems. This transformation is repeatable enabling subsequent biometric comparisons. This paper is introducing a new idea to exploit as a transformation function for cancelable biometrics aimed at protecting the templates against iterative optimization attacks. Our proposed scheme is based on time-varying keys (random biometrics in our case) and morphing tran…
School of Engineering Technology, SUNY at Farmingdale（纽约州立大学法明代尔分校工程技术学院）
Fingerprint is widely used in a variety of applications. Security measures have to be taken to protect the privacy of fingerprint data. Cancelable biometrics is proposed as an effective mechanism of using and protecting biometrics. In this paper we propose a new method of constructing cancelable fingerprint template by combining real template with synthetic template. Specifically, each user is given one synthetic minutia template generated with random number generator. Every minutia point from the real template is individually thrown into the synthetic template, from which its k-nearest neighb…
College of Computer and Information Technology（计算机与信息学院）；China Three Gorges University（三峡大学）
Cancelable biometric techniques have been used to prevent the compromise of biometric data by generating and using their corresponding cancelable templates for user authentication. However, the non-invertible distance preserving transformation methods employed in various schemes are often vulnerable to information leakage since matching is performed in the transformed domain. In this paper, we propose a non-invertible distance preserving scheme based on vector permutation and shift-order process. First, the dimension of feature vectors is reduced using kernelized principle component analysis (…
Idiap Research Institute（Idiap研究所）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Universidad Autonoma de Madrid (UAM)（马德里自治大学）；Hochschule Darmstadt (HDA)（达姆施塔特应用技术大学）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；Université de Lausanne (UNIL)（洛桑大学）
In this paper, we benchmark several cancelable biometrics (CB) schemes on different biometric characteristics. We consider BioHashing, Multi-Layer Perceptron (MLP) Hashing, Bloom Filters, and two schemes based on Index-of-Maximum (IoM) Hashing (i.e., IoM-URP and IoM-GRP). In addition to the mentioned CB schemes, we introduce a CB scheme (as a baseline) based on user-specific random transformations followed by binarization. We evaluate the unlinkability, irreversibility, and recognition performance (which are the required criteria by the ISO/IEC 24745 standard) of these CB schemes on deep learn…
Indian Institute of Technology Indore（印度理工学院印多尔分校）
In a biometric verification system, leakage of biometric data leads to permanent identity loss since original biometric data is inherently linked to a user. Further, various types of attacks on a biometric system may reveal the original template and utility in other applications. To address these security and privacy concerns cancelable biometric has been introduced. Cancelable biometric constructs a protected template from the original biometric template using transformation functions and performs the comparison between templates in the transformed domain. Recent approaches towards cancelable…
Université Clermont-Auvergne（克莱蒙奥弗涅大学）；CNRS（法国国家科学研究中心）；Mines de Saint-Étienne（圣艾蒂安高等矿业学校）；LIMOS（图像与系统建模与优化实验室）；LMBP（布莱士·帕斯卡数学实验室）
Cancelable biometric schemes aim at generating secure biometric templates by combining user specific tokens, such as password, stored secret or salt, along with biometric data. This type of transformation is constructed as a composition of a biometric transformation with a feature extraction algorithm. The security requirements of cancelable biometric schemes concern the irreversibility, unlinkability and revocability of templates, without losing in accuracy of comparison. While several schemes were recently attacked regarding these requirements, full reversibility of such a composition in ord…
Cancelable biometric schemes generate secure biometric templates by combining user specific tokens and biometric data. The main objective is to create irreversible, unlinkable, and revocable templates, with high accuracy in matching. In this paper, we cryptanalyze two recent cancelable biometric schemes based on a particular locality sensitive hashing function, index-of-max (IoM): Gaussian Random Projection-IoM (GRP-IoM) and Uniformly Random Permutation-IoM (URP-IoM). As originally proposed, these schemes were claimed to be resistant against reversibility, authentication, and linkability attac…
Anhui University（安徽大学）；Yonsei University（延世大学）；Monash University（莫纳什大学）；University of Sassari（萨萨里大学）
Cancelable biometrics (CB) employs an irreversible transformation to convert the biometric features into transformed templates while preserving the relative distance between two templates for security and privacy protection. However, distance preservation invites unexpected security issues such as pre-image attacks, which are often neglected.This paper presents a generalized pre-image attack method and its extension version that operates on practical CB systems. We theoretically reveal that distance preservation property is a vulnerability source in the CB schemes. We then propose an empirical…
National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所）；Shizuoka University（静冈大学）；Hitachi, Ltd.（日立有限公司）
A cancelable biometric scheme called correlation-invariant random filtering (CIRF) is known as a promising template protection scheme. This scheme transforms a biometric feature represented as an image via the 2D number theoretic transform (NTT) and random filtering. CIRF has perfect secrecy in that the transformed feature leaks no information about the original feature. However, CIRF cannot be applied to large-scale biometric identification, since the 2D inverse NTT in the matching phase requires high computational time. Furthermore, existing biometric indexing schemes cannot be used in conju…
Present world has already been consistently exploring the fine edges of online and digital world by imposing multiple challenging problems/scenarios. Similar to physical world, personal identity management is very crucial in-order to provide any secure online system. Last decade has seen a lot of work in this area using biometrics such as face, fingerprint, iris etc. Still there exist several vulnerabilities and one should have to address the problem of compromised biometrics much more seriously, since they cannot be modified easily once compromised. In this work, we have proposed a secure can…
Cancelable biometrics refers to a group of techniques in which the biometric inputs are transformed intentionally using a key before processing or storage. This transformation is repeatable enabling subsequent biometric comparisons. This paper introduces a new scheme for cancelable biometrics aimed at protecting the templates against potential attacks, applicable to any biometric-based recognition system. Our proposed scheme is based on time-varying keys obtained from morphing random biometric information. An experimental implementation of the proposed scheme is given for face biometrics. The …
School of Cyber Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Engineering Research Center of Blockchain Application, Supervision and Management (Southeast University), Ministry of Education（教育部区块链应用监督与管理工程研究中心（东南大学））；Purple Mountain Laboratories（紫金山实验室）；College of Computer, Nanjing University of Posts and Telecommunications（南京邮电大学计算机学院）；Department of Computer and Information Science, University of Macao（澳门大学计算机与信息科学系）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科学与技术大学计算机科学与工程系）
Indian Institute of Technology Indore（印度理工学院印多尔分校）
The identity of a user is permanently lost if biometric data gets compromised since the biometric information is irreplaceable and irrevocable. To revoke and reissue a new template in place of the compromised biometric template, the idea of cancelable biometrics has been introduced. The concept behind cancelable biometric is to irreversibly transform the original biometric template and perform the comparison in the protected domain. In this paper, a coprime transformation scheme has been proposed to derive a protected fingerprint template. The method divides the fingerprint region into a numbe…
Monash University Malaysia（莫纳什大学马来西亚校区）；Electronics and Telecommunications Research Institute（韩国电子通信研究院）；Yonsei University（延世大学）
In this paper, we propose a ranking based locality sensitive hashing inspired two-factor cancelable biometrics, dubbed "Index-of-Max" (IoM) hashing for biometric template protection. With externally generated random parameters, IoM hashing transforms a real-valued biometric feature vector into discrete index (max ranked) hashed code. We demonstrate two realizations from IoM hashing notion, namely Gaussian Random Projection based and Uniformly Random Permutation based hashing schemes. The discrete indices representation nature of IoM hashed codes enjoy serveral merits. Firstly, IoM hashing empo…
When compared to unimodal systems, multimodal biometric systems have several advantages, including lower error rate, higher accuracy, and larger population coverage. However, multimodal systems have an increased demand for integrity and privacy because they must store multiple biometric traits associated with each user. In this paper, we present a deep learning framework for feature-level fusion that generates a secure multimodal template from each user's face and iris biometrics. We integrate a deep hashing (binarization) technique into the fusion architecture to generate a robust binary mult…
CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition
School of Cyber Science and Engineering, Southeast University（东南大学网络空间科学与工程学院）；Purple Mountain Laboratories（紫金山实验室）；University of Macao（澳门大学）；The Hong Kong University of Science and Technology（香港科技大学）
