Secure and Privacy Preserving Proxy Biometrics Identities
- URL: http://arxiv.org/abs/2212.10812v1
- Date: Wed, 21 Dec 2022 07:02:11 GMT
- Title: Secure and Privacy Preserving Proxy Biometrics Identities
- Authors: Harkeerat Kaur, Rishabh Shukla, Isao Echizen and Pritee Khanna
- Abstract summary: This work proposes a novel approach for generating new artificial fingerprints also called proxy fingerprints.
These proxy biometrics can be generated from original ones only with the help of a user-specific key.
Using the proposed approach a proxy dataset is generated from samples belonging to Anguli fingerprint database.
Matching experiments were performed on the new set which is 5 times larger than the original, and it was found that their performance is at par with 0 FAR and 0 FRR in the stolen key, safe key scenarios.
- Score: 25.272389610447856
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With large-scale adaption to biometric based applications, security and
privacy of biometrics is utmost important especially when operating in
unsupervised online mode. This work proposes a novel approach for generating
new artificial fingerprints also called proxy fingerprints that are natural
looking, non-invertible, revocable and privacy preserving. These proxy
biometrics can be generated from original ones only with the help of a
user-specific key. Instead of using the original fingerprint, these proxy
templates can be used anywhere with same convenience. The manuscripts walks
through an interesting way in which proxy fingerprints of different types can
be generated and how they can be combined with use-specific keys to provide
revocability and cancelability in case of compromise. Using the proposed
approach a proxy dataset is generated from samples belonging to Anguli
fingerprint database. Matching experiments were performed on the new set which
is 5 times larger than the original, and it was found that their performance is
at par with 0 FAR and 0 FRR in the stolen key, safe key scenarios. Other
parameters on revocability and diversity are also analyzed for protection
performance.
Related papers
- A secure and private ensemble matcher using multi-vault obfuscated templates [1.3518297878940662]
Generative AI has revolutionized modern machine learning by providing unprecedented realism, diversity, and efficiency in data generation.
Biometric template security and secure matching are among the most sought-after features of modern biometric systems.
This paper proposes a novel obfuscation method using Generative AI to enhance biometric template security.
arXiv Detail & Related papers (2024-04-08T05:18:39Z) - PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via
Secure Flow [69.78820726573935]
We name it PRO-Face S, short for Privacy-preserving Reversible Obfuscation of Face images via Secure flow-based model.
In the framework, an Invertible Neural Network (INN) is utilized to process the input image along with its pre-obfuscated form, and generate the privacy protected image that visually approximates to the pre-obfuscated one.
arXiv Detail & Related papers (2023-07-18T10:55:54Z) - FedForgery: Generalized Face Forgery Detection with Residual Federated
Learning [87.746829550726]
Existing face forgery detection methods directly utilize the obtained public shared or centralized data for training.
The paper proposes a novel generalized residual Federated learning for face Forgery detection (FedForgery)
Experiments conducted on publicly available face forgery detection datasets prove the superior performance of the proposed FedForgery.
arXiv Detail & Related papers (2022-10-18T03:32:18Z) - Hierarchical Perceptual Noise Injection for Social Media Fingerprint
Privacy Protection [106.5308793283895]
fingerprint leakage from social media raises a strong desire for anonymizing shared images.
To guard the fingerprint leakage, adversarial attack emerges as a solution by adding imperceptible perturbations on images.
We propose FingerSafe, a hierarchical perceptual protective noise injection framework to address the mentioned problems.
arXiv Detail & Related papers (2022-08-23T02:20:46Z) - Synthesis and Reconstruction of Fingerprints using Generative
Adversarial Networks [6.700873164609009]
We propose a novel fingerprint synthesis and reconstruction framework based on the StyleGan2 architecture.
We also derive a computational approach to modify the attributes of the generated fingerprint while preserving their identity.
The proposed framework was experimentally shown to outperform contemporary state-of-the-art approaches for both fingerprint synthesis and reconstruction.
arXiv Detail & Related papers (2022-01-17T00:18:00Z) - Authentication Attacks on Projection-based Cancelable Biometric Schemes [0.6499759302108924]
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.
The security requirements of cancelable biometric schemes concern the irreversibility, unlinkability and revocability of templates, without losing in accuracy of comparison.
In this paper, we formalize these attacks for a traditional cancelable scheme with the help of integer linear programming (ILP) and quadratically constrained quadratic programming (QCQP)
arXiv Detail & Related papers (2021-10-28T14:39:35Z) - Responsible Disclosure of Generative Models Using Scalable
Fingerprinting [70.81987741132451]
Deep generative models have achieved a qualitatively new level of performance.
There are concerns on how this technology can be misused to spoof sensors, generate deep fakes, and enable misinformation at scale.
Our work enables a responsible disclosure of such state-of-the-art generative models, that allows researchers and companies to fingerprint their models.
arXiv Detail & Related papers (2020-12-16T03:51:54Z) - Keystroke Biometrics in Response to Fake News Propagation in a Global
Pandemic [77.79066811371978]
This work proposes and analyzes the use of keystroke biometrics for content de-anonymization.
Fake news have become a powerful tool to manipulate public opinion, especially during major events.
arXiv Detail & Related papers (2020-05-15T17:56:11Z) - Latent Fingerprint Registration via Matching Densely Sampled Points [100.53031290339483]
Existing latent fingerprint registration approaches are mainly based on establishing correspondences between minutiae.
We propose a non-minutia latent fingerprint registration method which estimates the spatial transformation between a pair of fingerprints.
The proposed method achieves the state-of-the-art registration performance, especially under challenging conditions.
arXiv Detail & Related papers (2020-05-12T15:51:59Z) - SynFi: Automatic Synthetic Fingerprint Generation [23.334625222079634]
We introduce a new approach to automatically generate high-fidelity synthetic fingerprints at scale.
We show that our methodology is the first to generate fingerprints that are computationally indistinguishable from real ones.
arXiv Detail & Related papers (2020-02-16T07:45:29Z) - An Overview of Fingerprint-Based Authentication: Liveness Detection and
Beyond [0.0]
We focus on methods to detect physical liveness, defined as techniques that can be used to ensure that a living human user is attempting to authenticate on a system.
We analyze how effective these methods are at preventing attacks where a malicious entity tries to trick a fingerprint-based authentication system to accept a fake finger as a real one.
arXiv Detail & Related papers (2020-01-24T20:07:53Z)
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.