Determining the impact of adversarial cyberattacks on the performance of a comprehensive biometric identification method based on local-texture descriptors

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.363911

Keywords:

biometric identification, facial recognition, image processing, software, cyber threats, adversarial attacks, local texture descriptors, HOG, 1DLBP, occlusive perturbations

Abstract

The object of study is a comprehensive biometric identification method based on local-texture descriptors HOG and 1DLBP. The task addressed is to determine the impact of adversarial cyberattacks on the accuracy of biometric identification by facial image.

The results under the predefined research conditions were evaluated in terms of the comprehensive method's efficiency, robustness, and stability. Experiments were conducted on six datasets covering controlled and uncontrolled shooting conditions, using a unified set of metrics. The impact was determined in scenarios of full visibility of facial features and in the presence of local occlusive disturbances characteristic of adversarial attacks.

The efficiency retention coefficient of the comprehensive method when used under controlled shooting conditions is 86.84–92.86% with a sensitivity index of 7.14–13.16%; the decrease in accuracy is statistically insignificant for most image sets. Compared with DNNs whose accuracy degradation under the influence of adversarial attacks reaches 26.45–76%, the comprehensive method's identification accuracy decreases by 1.5%. Such results are due to the features of the algorithmic formation of attribute vectors by descriptors and the comprehensive method's absence of sensitivity to perturbations calculated on the properties of DNN methods.

The HOG and 1DLBP descriptors compute the gradient and texture characteristics of local image regions based on deterministic algorithms without using training parameters and the error backpropagation mechanism. As a result, adversarial perturbations optimized for hierarchical nonlinear representations of DNNs have a limited impact on the feature space formed by descriptors. By conducting a study on face images acquired under variable conditions, the limits of the solution's applicability were determined.

The suitability of the comprehensive method for practical application in cybersecurity complexes, in particular in video surveillance, access control, and checkpoint systems, has been established

Author Biographies

Yelyzaveta Zhabska, Taras Shevchenko National University of Kyiv

Doctor of Philosophy (PhD)

Department of Software Systems and Technologies

Kateryna Merkulova, Taras Shevchenko National University of Kyiv

Candidate of Technical Sciences, Associate Professor

Department of Software Systems and Technologies

Oleksii Bychkov, Taras Shevchenko National University of Kyiv

Doctor of Technical Sciences, Professor

Department of Software Systems and Technologies

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Determining the impact of adversarial cyberattacks on the performance of a comprehensive biometric identification method based on local-texture descriptors

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Published

2026-06-30

How to Cite

Zhabska, Y., Merkulova, K., & Bychkov, O. (2026). Determining the impact of adversarial cyberattacks on the performance of a comprehensive biometric identification method based on local-texture descriptors. Eastern-European Journal of Enterprise Technologies, 3(9 (141), 26–37. https://doi.org/10.15587/1729-4061.2026.363911

Issue

Vol. 3 No. 9 (141) (2026): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2026 Yelyzaveta Zhabska, Kateryna Merkulova, Oleksii Bychkov

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