TY  - JOUR
T1  - ELA (Error Level Analysis) kullanarak CNN Tabanlı Görüntü Sahteciliği Tespiti
TT  - CNN-Based Image Forgery Detection Using ELA (Error Level Analysis)
AU  - Osmanov, Elnur
AU  - Çetin Taş, İclal
PY  - 2025
DA  - April
Y2  - 2025
DO  - 10.46387/bjesr.1580950
JF  - Mühendislik Bilimleri ve Araştırmaları Dergisi
JO  - BJESR
PB  - Bandırma Onyedi Eylül Üniversitesi
WT  - DergiPark
SN  - 2687-4415
SP  - 41
EP  - 50
VL  - 7
IS  - 1
LA  - tr
AB  - Günümüzde görüntü sahteciliği, dijital ortamda bilgi güvenliğini tehdit eden önemli bir sorun haline gelmiştir. Dijital medyanın hızla yayılmasıyla birlikte, görüntü manipülasyonları artış göstermiş ve güvenilir bilgi paylaşımını zorlaştırmıştır. Bu bağlamda, görüntü sahteciliği, dijital çağın en önemli bilgi güvenliği sorunlarından biri olarak öne çıkmaktadır.Bu çalışmada, Hata Seviyesi Analizi (ELA) ve Derin Sinir Ağları (CNN) kullanılarak görüntü sahteciliğinin tespit edilmesi amaçlanmıştır. İlk olarak ELA'nın temel prensipleri ve uygulama yöntemleri ele alınmış, ardından CNN'in mimarisi ve eğitim süreçleri detaylandırılmıştır. Casia, Columbia ve hibrit veri setleri üzerinde gerçekleştirilen deneyler sonucunda, önerilen yöntemin yüksek doğruluk oranlarına ulaştığı gözlemlenmiştir. Elde edilen bulgular, ELA ve CNN kombinasyonunun görüntü sahteciliği tespitinde etkili bir araç olduğunu ortaya koymaktadır. Deneyler sonunda CNN vee la beraber kullanıldığından en yüksek F1-skor değeri 0.94 bulunmuştur.
KW  - Derin Öğrenme
KW  - Görüntü Sahteciliği Tespiti
KW  - ELA
KW  - CNN
N2  - In today's world, image forgery has become a significant issue threatening information security in digital environments. With the rapid spread of digital media, instances of image manipulation have increased, making reliable information sharing more difficult. In this context, image forgery emerges as one of the most critical information security issues of the digital age.This study aims to detect image forgery using Error Level Analysis (ELA) and Convolutional Neural Networks (CNN). Initially, the fundamental principles and application methods of ELA are discussed, followed by a detailed examination of the architecture and training processes of CNN. Experiments conducted on the Casia, Columbia, and hybrid datasets have shown that the proposed method achieves high accuracy rates. The findings indicate that the combination of ELA and CNN serves as an effective tool for detecting image forgery. The F1 Score obtained at the end of the experiments was calculated to be 0.94, emphasizing the method's effectiveness.
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UR  - https://doi.org/10.46387/bjesr.1580950
L1  - https://dergipark.org.tr/tr/download/article-file/4346936
ER  -