DETECTION OF PRINTED FALSE ATTACKS USING NEURAL NETWORKS

This article discusses a method for detecting false positives against a biometric facial recognition system based on deep convolutional neural networks. The proposed method is designed to detect printed false positives and is tested on open databases of real and fake faces, and the results are analyzed. The types of false positive attacks launched against a biometric system based on existing faces are analyzed.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-03
DOI
https://doi.org/10.5281/zenodo.17258688
Primary Topic
Milk Quality and Mastitis in Dairy Cows
Type
article
Field-Weighted Citation Impact
0.00
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article

DETECTION OF PRINTED FALSE ATTACKS USING NEURAL NETWORKS

Zenodo (CERN European Organization for Nuclear Research)
Milk Quality and Mastitis in Dairy Cows
article

DETECTION OF PRINTED FALSE ATTACKS USING NEURAL NETWORKS

article en

Abstract

This article discusses a method for detecting false positives against a biometric facial recognition system based on deep convolutional neural networks. The proposed method is designed to detect printed false positives and is tested on open databases of real and fake faces, and the results are analyzed. The types of false positive attacks launched against a biometric system based on existing faces are analyzed.

Zenodo (CERN European Organization for Nuclear Research)
Kurgan State University (RU)
Peace, Justice and strong institutions
Openalex Percentile: Top 39%
Milk Quality and Mastitis in Dairy Cows
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DETECTION OF PRINTED FALSE ATTACKS USING NEURAL NETWORKS · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS