Classifying Real and AI-Generated Images Using Fine-Tuned ResNet50

Due to advancements in technology, especially in Artificial Intelligence, it has become increasingly difficult for humans to distinguish between real and AI-generated images. Technologies like Generative Adversarial Networks (GANs) and Latent Diffusion Models (LDMs) can generate images so realistic that humans often cannot identify them as AI-generated. This paper addresses the challenge of identifying AI-generated and real images by presenting a Fine-Tuned ResNet50 model. The model is trained and tested ona dataset of 140K real and fake face images, demonstrating its effectiveness in image classification.

Authors

Publication Details

Published
2025-01-01
DOI
https://doi.org/10.56147/aaiet.1.2.7
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Classifying Real and AI-Generated Images Using Fine-Tuned ResNet50

Khan Aabid Abdal Mashkoor
Neural Networks and Applications
article

Classifying Real and AI-Generated Images Using Fine-Tuned ResNet50

Khan Aabid Abdal Mashkoor
article en

Abstract

Due to advancements in technology, especially in Artificial Intelligence, it has become increasingly difficult for humans to distinguish between real and AI-generated images. Technologies like Generative Adversarial Networks (GANs) and Latent Diffusion Models (LDMs) can generate images so realistic that humans often cannot identify them as AI-generated. This paper addresses the challenge of identifying AI-generated and real images by presenting a Fine-Tuned ResNet50 model. The model is trained and tested ona dataset of 140K real and fake face images, demonstrating its effectiveness in image classification.

Vol. 01(02)
Openalex Percentile: Top 95%
Neural Networks and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Classifying Real and AI-Generated Images Using Fine-Tuned ResNet50 — Khan Aabid Abdal Mashkoor · (2025) | TGRS Research Map | TGRS