Veriface AI: A Deep Learning-Based Approach to Deepfake Detection for Image Authenticity Validation

Kumar, V.Dinesh Kumar , A.kalam , K.Kishore

doi:https://doi.org/10.55041/ijsmt.v2i2.050

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VERIFACE AI: A DEEP LEARNING-BASED APPROACH TO DEEPFAKE DETECTION FOR IMAGE AUTHENTICITY VALIDATION

AUTHORS:

V.Dinesh Kumar , A.kalam , K.Kishore Kumar

Mentor

Affiliation

dept of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation

DOI: 10.55041/ijsmt.v2i2.050

CC BY 4.0 License:

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

This paper presents VeriFace AI, a deep learning-based system for detecting AI- generated fake faces. Leveraging a convolutional neural network (CNN) architecture trained on 1,400 images, the system achieves an accuracy of 83.2%. It addresses the growing concern of deepfake technology and its potential impact on digital media authenticity. We highlight the system's deployment as a web- based tool for real-time image analysis, aiming to mitigate the challenges posed by deepfake proliferation.

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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.

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