CLASSIFICATION OF REAL AND FAKE AI-GENERATED IMAGES USING CNN

The rapid development of artificial intelligence (AI) in producing realistic images has raised concerns over the use of such technology. In this study, we give a detailed explanation of real and AI-generated photos to develop a classification model that can distinguish between the two. Our primary objective is to create a strong method to identify AI-generated images, which can have tremendous implications for various applications, including content moderation and image forensics. We create a dataset of autistic images from diverse sources and AI-generated images created with stateof-the-art generative models. To extract their distinct features, we extract different features from these images, such as color histograms, texture features, and deep neural network features. We train and validate the convolutional neural network (CNN) machine learning model to classify the images. Our results affirm the effectiveness of the CNN model, with a 95.5% accuracy in detecting real and fake photos.

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Publication Details

Journal
International Journal of Engineering Research and Science & Technology
Published
2025-09-25
DOI
https://doi.org/10.62643/ijerst.2025.v21.n3(1).pp1803-1810
Primary Topic
Digital Media Forensic Detection
Type
article
Field-Weighted Citation Impact
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article

CLASSIFICATION OF REAL AND FAKE AI-GENERATED IMAGES USING CNN

K.R. Vasanth, Vinod Kumar, M. Jayaram, P. P. Abhilash et al.
International Journal of Engineering Research and Science & Technology
Digital Media Forensic Detection
article

CLASSIFICATION OF REAL AND FAKE AI-GENERATED IMAGES USING CNN

K.R. Vasanth, Vinod Kumar, M. Jayaram, P. P. Abhilash, Kacham Saiesh
article en

Abstract

The rapid development of artificial intelligence (AI) in producing realistic images has raised concerns over the use of such technology. In this study, we give a detailed explanation of real and AI-generated photos to develop a classification model that can distinguish between the two. Our primary objective is to create a strong method to identify AI-generated images, which can have tremendous implications for various applications, including content moderation and image forensics. We create a dataset of autistic images from diverse sources and AI-generated images created with stateof-the-art generative models. To extract their distinct features, we extract different features from these images, such as color histograms, texture features, and deep neural network features. We train and validate the convolutional neural network (CNN) machine learning model to classify the images. Our results affirm the effectiveness of the CNN model, with a 95.5% accuracy in detecting real and fake photos.

International Journal of Engineering Research and Science & TechnologyVol. 21(3 (1))
Openalex Percentile: Top 74%
Digital Media Forensic Detection
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CLASSIFICATION OF REAL AND FAKE AI-GENERATED IMAGES USING CNN — K.R. Vasanth, Vinod Kumar, et al. · International Journal of Engineering Research and Science & Technology (2025) | TGRS Research Map | TGRS