keyword
fingerprint verification
Fingerprint verification is a biometric authentication process that confirms an individual's claimed identity by conducting a one-to-one comparison between a newly captured fingerprint and a previously enrolled reference template. Unlike fingerprint identification, which performs a one-to-many search across an entire database to establish who an unknown person is, verification evaluates whether the submitted sample matches the specific record associated with the user. The standard verification workflow involves digital image acquisition, image enhancement to clarify ridge and valley patterns, extraction of distinctive biometric features such as minutiae points, and algorithmic matching that computes a similarity score against a threshold to either accept or reject the identity claim.
2 items
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations
Zirui Peng, Shaofeng Li, Guoxing Chen, Cheng Zhang, Haojin Zhu, Minhui Xue
Why you should read this
Proposes a practical deep neural network fingerprinting framework that leverages universal adversarial perturbations to capture global decision boundary geometry, enabling model owners to detect intellectual property theft through black-box queries with over 99.99% confidence.
In this paper, we propose a novel and practical mechanism to enable the service provider to verify whether a suspect model is stolen from the victim model via model extraction attacks. Our key insight is that the profile of a DNN model's decision boundary can be uniquely characterized by its Universal Adversarial Perturbations (UAPs). UAPs belong to a low-dimensional subspace and piracy models' subspaces are more consistent with victim model's subspace compared with non-piracy model. Based on this, we propose a UAP fingerprinting method for DNN models and train an encoder via contrastive learning that takes fingerprints as inputs, outputs a similarity score. Extensive studies show that our framework can detect model Intellectual Property (IP) breaches with confidence > 99.99 % within only 20 fingerprints of the suspect model. It also has good generalizability across different model architectures and is robust against post-modifications on stolen models.
Added
2026-09-26
Fingerprint Image Enhancement: Algorithm and Performance Evaluation
Lin Hong, Yifei Wan, Anil K. Jain
Why you should read this
Presents an adaptive ridge-frequency and orientation enhancement method that measurably improves fingerprint minutiae quality and verification accuracy.
A critical step in automatic fingerprint matching is to automatically and reliably extract minutiae from the input fingerprint images. However, the performance of a minutiae extraction algorithm relies heavily on the quality of the input fingerprint images. In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.
Source
https://www.cse.msu.edu/~rossarun/BiometricsTextBook/Papers/Fingerprint/HongFpImageEnhancement.pdfAdded
2026-09-14