Deep fake detection and classification using error-level analysis and deep learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252929" target="_blank" >RIV/61989100:27240/23:10252929 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-023-34629-3" target="_blank" >https://www.nature.com/articles/s41598-023-34629-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-023-34629-3" target="_blank" >10.1038/s41598-023-34629-3</a>

Result language

angličtina

Original language name

Deep fake detection and classification using error-level analysis and deep learning

Original language description

Due to the wide availability of easy-to-access content on social media, along with the advanced tools and inexpensive computing infrastructure, has made it very easy for people to produce deep fakes that can cause to spread disinformation and hoaxes. This rapid advancement can cause panic and chaos as anyone can easily create propaganda using these technologies. Hence, a robust system to differentiate between real and fake content has become crucial in this age of social media. This paper proposes an automated method to classify deep fake images by employing Deep Learning and Machine Learning based methodologies. Traditional Machine Learning (ML) based systems employing handcrafted feature extraction fail to capture more complex patterns that are poorly understood or easily represented using simple features. These systems cannot generalize well to unseen data. Moreover, these systems are sensitive to noise or variations in the data, which can reduce their performance. Hence, these problems can limit their usefulness in real-world applications where the data constantly evolves. The proposed framework initially performs an Error Level Analysis of the image to determine if the image has been modified. This image is then supplied to Convolutional Neural Networks for deep feature extraction. The resultant feature vectors are then classified via Support Vector Machines and K-Nearest Neighbors by performing hyper-parameter optimization. The proposed method achieved the highest accuracy of 89.5% via Residual Network and K-Nearest Neighbor. The results prove the efficiency and robustness of the proposed technique; hence, it can be used to detect deep fake images and reduce the potential threat of slander and propaganda.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Volume of the periodical

13

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

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UT code for WoS article

001001538500058

EID of the result in the Scopus database

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