AI Powered Fake Image and Video Detection with Forensic Analysis

January 1, 2026JOURNAL OF EMERGING TRENDS AND NOVEL RESEARCHOpen Access

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Key Points

This research aims to develop an accurate AI-powered system for detecting fake images and videos.
Utilized transfer learning with the EfficientNet-B4 architecture.
Trained on a diverse dataset of 330,335 images covering multiple domains.
Implemented deep learning pipeline in PyTorch with Mixed Precision Training.
Achieved high classification accuracy significantly better than standard convolutional neural networks.
Demonstrated effective image classification across human portraits, landscapes, architectural structures, and anime-style illustrations.
Incorporated Grad-CAM for visual explanations of model decisions, enhancing interpretability.

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Evaluates AI-based detection of fake images in various domains, suggesting improved accuracy.

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Gadekar et al. (2026) studied this question.

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