AUTOMATION IS A PROCEDURE FOR DEEPFAKE IMAGE DETECTION USING NEURAL NETWORKS

M. V. Babicheva, I. A. Tretiakov

About the authors

M. V. Babicheva, I. A. Tretiakov

Бабичева М. В. – кандидат технических наук, доцент кафедры радиофизики и инфокоммуникационных технологий ФГБОУ ВО «Донецкий государственный университет», 283001, г. Донецк, ул. Университетская, 24. Область научных интересов: информационная безопасность, пентестинг, нейронные сети.

Третьяков И. А. – кандидат технических наук, доцент, доцент кафедры радиофизики и инфокоммуникационных технологий ФГБОУ ВО «Донецкий государственный университет», 283001, г. Донецк, ул. Университетская, 24. Область научных интересов: автоматизация научных исследований и автоматизированные системы; оптические информационные технологии; методы и системы защиты информации, информационная безопасность.

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UDC: 004.932.2
DOI: 10.24412/2413-7383-94-105
Language: Russian

Abstract: сеть, Error Level Analysis Deepfakes are often used as tools for committing crimes against individuals and states, necessitating technical means to determine the artificial origin of images. This study explores various approaches to detecting fake images, including texture analysis, neural networks, and anomaly detection algorithms. The goal of the research is to investigate methods of creating fake images, identify their distinguishing features, and develop a d etection methodology using deep learning and neural networks. For generating deepfakes, a generative adversarial network (GAN) was employed, while a convolutional neural network (CNN) was used for recognition. The proposed model achieves 89% accuracy in detecting fake images, performing on par with most foreign counterparts.

Keywords: deepfakes, generative adversarial network (GAN), convolutional neural network (CNN), Error Level Analysis

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Issue: 1(36)'2025
Section: МЕТОДЫ И СИСТЕМЫ ЗАЩИТЫ ИНФОРМАЦИИ, ИНФОРМАЦИОННАЯ БЕЗОПАСНОСТЬ
How to cite: M. V. Babicheva, I. A. Tretiakov. AUTOMATION IS A PROCEDURE FOR DEEPFAKE IMAGE DETECTION USING NEURAL NETWORKS // Problems of Artificial Intelligence. - 2025. - № 1 (36). - P. 94-105. - https://paijournal.guiaidn.ru/en/2025/1(36)-8.html

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