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All Journal Jurnal Masyarakat Informatika
Aris Sugiharto
Department of Informatics, Universitas Diponegoro |Universitas Diponegoro
Computer Science & IT

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Journal : Jurnal Masyarakat Informatika

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Classification of Real and Fake Images Using Error Level Analysis Technique and MobileNetV2 Architecture Muhamad Nur Baihaqi; Aris Sugiharto; Henri Tantyoko
Jurnal Masyarakat Informatika Vol 16, No 1 (2025): May 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.1.73283

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.
Co-Authors Henri Tantyoko Muhamad Nur Baihaqi