DOI: https://doi.org/10.14710/jmasif.16.1.73283

Classification of Real and Fake Images Using Error Level Analysis Technique and MobileNetV2 Architecture

Department of Informatics, Indonesia

Received: 15 May 2025; Revised: 26 May 2025; Accepted: 28 May 2025; Available online: 28 May 2025; Published: 31 May 2025.
Editor(s): Ferda Ernawan
Open Access Copyright (c) 2025 The authors. Published by Department of Informatics Universitas, Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

Advancements in technology have made image forgery increasingly difficult to detect, raising the risk of misinformation on social media. To address this issue, Error Level Analysis (ELA) feature extraction can be utilized to detect error level variations in lossy-formatted images such as JPEG. This study evaluates the contribution of ELA features in classifying authentic and forged images using the MobileNetV2 model. Two scenarios were tested using the CASIA 2.0 dataset: without ELA and with ELA. Fine-tuning was performed to adapt the model to the new problem. Experimental results show that incorporating ELA improves model accuracy up to 93.1%, compared to only 76.41% in the scenario without ELA. Validation using k-fold cross-validation yielded a high average f1-score of 96.83%, confirming the effectiveness of ELA in enhancing the classification performance of authentic and forged images.

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Keywords: classification;error level analysis;mobilenetv2;fine tuning;k-fold cross validation;pretrained model

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Section: Research Article
Language : EN
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