PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

• URL: http://arxiv.org/abs/2404.09214v1
• Date: Sun, 14 Apr 2024 10:55:15 GMT
• Title: PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound
• Authors: Man Zhou, Shuao Su, Qian Wang, Qi Li, Yuting Zhou, Xiaojing Ma, Zhengxiong Li,
• Abstract summary: We propose a new side-channel attack on the minutiae-based Automatic Fingerprint Identification System (AFIS) The attack scenario of PrintListener is extensive and covert. It only needs to record users' fingertip friction sound and can be launched by leveraging a large number of social media platforms.
• Score: 23.149939556959772
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Fingerprint authentication has been extensively employed in contemporary identity verification systems owing to its rapidity and cost-effectiveness. Due to its widespread use, fingerprint leakage may cause sensitive information theft, enormous economic and personnel losses, and even a potential compromise of national security. As a fingerprint that can coincidentally match a specific proportion of the overall fingerprint population, MasterPrint rings the alarm bells for the security of fingerprint authentication. In this paper, we propose a new side-channel attack on the minutiae-based Automatic Fingerprint Identification System (AFIS), called PrintListener, which leverages users' fingertip swiping actions on the screen to extract fingerprint pattern features (the first-level features) and synthesizes a stronger targeted PatternMasterPrint with potential second-level features. The attack scenario of PrintListener is extensive and covert. It only needs to record users' fingertip friction sound and can be launched by leveraging a large number of social media platforms. Extensive experimental results in realworld scenarios show that Printlistener can significantly improve the attack potency of MasterPrint.

Related papers

• Deep Learning-Based Approaches for Contactless Fingerprints Segmentation and Extraction [1.2441902898414798]
  We develop a deep learning-based segmentation tool for contactless fingerprint localization and segmentation. In our evaluation, our segmentation method demonstrated an average mean absolute error (MAE) of 30 pixels, an error in angle prediction (EAP) of 5.92 degrees, and a labeling accuracy of 97.46%.
  arXiv  Detail & Related papers  (2023-11-26T01:56:10Z)
• RFDforFin: Robust Deep Forgery Detection for GAN-generated Fingerprint Images [45.73061833269094]
  We propose the first deep forgery detection approach for fingerprint images, which combines unique ridge features of fingerprint and generation artifacts of the GAN-generated images. Our proposed approach is effective and robust with low complexities.
  arXiv  Detail & Related papers  (2023-08-18T04:05:18Z)
• Fingerprint Liveness Detection using Minutiae-Independent Dense Sampling of Local Patches [0.0]
  Fingerprint recognition and matching is a common form of user authentication. Spoof detection and liveness detection algorithms are being researched as countermeasures to this security vulnerability. This paper introduces a fingerprint anti-spoofing mechanism using machine learning.
  arXiv  Detail & Related papers  (2023-04-11T16:11:44Z)
• 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)
• FIGO: Enhanced Fingerprint Identification Approach Using GAN and One Shot Learning Techniques [0.0]
  We propose a Fingerprint Identification approach based on Generative adversarial network and One-shot learning techniques. First, we propose a Pix2Pix model to transform low-quality fingerprint images to a higher level of fingerprint images pixel by pixel directly in the fingerprint enhancement tier. Second, we construct a fully automated fingerprint feature extraction model using a one-shot learning approach to differentiate each fingerprint from the others in the fingerprint identification process.
  arXiv  Detail & Related papers  (2022-08-11T02:45:42Z)
• A review of schemes for fingerprint image quality computation [66.32254395574994]
  This paper reviews existing approaches for fingerprint image quality computation. We also implement, test and compare a selection of them using the MCYT database including 9000 fingerprint images.
  arXiv  Detail & Related papers  (2022-07-12T10:34:03Z)
• On the vulnerability of fingerprint verification systems to fake fingerprint attacks [57.36125468024803]
  A medium-size fake fingerprint database is described and two different fingerprint verification systems are evaluated on it. Results for an optical and a thermal sweeping sensors are given.
  arXiv  Detail & Related papers  (2022-07-11T12:22:52Z)
• FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement [23.67808389519383]
  We propose a new method that formulates the latent fingerprint enhancement as a constrained fingerprint generation problem. Experimental results on two public latent fingerprint databases demonstrate that our method outperforms the state of the arts significantly.
  arXiv  Detail & Related papers  (2022-06-26T14:05:21Z)
• 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)
• 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)

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.