International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 3
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GENERATIVE AI "DEEPFAKE" VIDEO & IMAGE VERIFIER

Author(s) Mr. Madhav Goyal, Mr. Vedant Joshi, Ms. Meenu
Country India
Abstract The rapid growth of generative Artificial Intelligence has led to the large distribution of "deepfakes"—synthetic media generated via deep learning to outline events, statements, or individuals that never actually existed. The
dangerous part of this technology is that it is a threat to digital security, contributing to extortion, financial fraud, political manipulation, and a broader erosion of public trust. This model proposes a reliable, deep learning-based verification system designed to detect and classify deepfake facial images.
It uses Convolutional Neural Networks (CNNs) to process user-uploaded images and extract minute visual features, analyzing them for anomalies such as texture inconsistencies, synthetic skin lacking natural microdetail, and non-natural pixel noise distributions. To ensure user transparency and trust, the verifier incorporates an explainable AI framework.Unlike acting as a "black box," the system outputs a definitive confidence score alongside a diagnostic heatmap
that visually highlights the specific manipulated areas. Also, it provides explicit reasoning for its detection outcomes, such as identifying statistical distributions that match known Generative Adversarial Network GAN)outputs. Finally, this model demonstrates a highly practical application of AI to combat cybercrime and verify theauthenticity of digital media
Keywords Deepfake Detection, Generative AI, Synthetic Media, Convolutional Neural Networks (CNN), Image Verification, Digital Forensics, Facial Manipulation, Feature Extraction, Explainable AI (XAI), Heatmap Visualization, Generative Adversarial Networks (GAN), Cybercrime Prevention, Misinformation Mitigation.
Field Engineering
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-15
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E-ISSN 2582-2160
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