What question did this study set out to answer?

The aim is to leverage deep learning for accurately identifying deep fake images, enhancing media authenticity.

April 10, 2026Open Access

Deep learning-based identification of deep fake images

Key Points

The aim is to leverage deep learning for accurately identifying deep fake images, enhancing media authenticity.
Developed deep learning algorithms for image analysis
Utilized a dataset comprising real and deep fake images
Trained models to differentiate between genuine and manipulated images
Achieved high accuracy rates in identifying deep fake images
Demonstrated improved performance over traditional methods
Reduced false positive and negative rates significantly

Abstract

UGC-CARE list, New UGC-CARE Reference List, UGC CARE Journals, ugc care list of journal, ugc care list, UGC Approved list, list of ugc approved journal, ugc approved journal,IJRAR - international Research Journal,IJRAR.ORG,Ijrar.org, International Journal of Research and Analytical Reviews (IJRAR) , UGC Approved journal, ugc approved,ugc, ugc certify, publish free of cost, free publication, UGC and ISSN Approved , International Peer Reviewed, Open Access Journal , e ISSN 2348 –1269, Print ISSN 2349-5138, ISSN: 2348 –1269, Impact Factor : 5.75 , E- journal, Low Cost INR 500, Free Publication

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Cite This Study

MAHAR et al. (Wed,) studied this question.

synapsesocial.com/papers/69d8948f6c1944d70ce05710 https://doi.org/https://doi.org/10.56975/ijrar.v12i3.318864

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