[2605.08167] Digital Image Forgery Detection Using Transfer Learning
[Submitted on 4 May 2026]
Title:Digital Image Forgery Detection Using Transfer Learning
View a PDF of the paper titled Digital Image Forgery Detection Using Transfer Learning, by Fatma Betul Buyuk and Gozde Karatas Baydogmus and Ali Buldu and Ayaulym Tulendiyeva and Zhuldyz Baizhumanova
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Abstract:The increasing availability of advanced image editing tools has led to a significant rise in manipulated digital content, posing serious challenges for digital forensics and information security. This study presents a transfer learning-based framework for digital image forgery detection that integrates compression-aware feature enhancement with deep convolutional neural network (CNN) architectures.
The proposed approach introduces a hybrid input representation that combines RGB images with compression difference-based features (FDIFF), explicitly highlighting subtle manipulation artifacts that are often difficult to detect. In addition, a model-specific adaptive threshold optimization strategy based on the Youden Index is employed to improve classification reliability by achieving a better balance between true positive and false positive rates.
Experiments conducted on the CASIA v2.0 dataset using multiple pretrained CNN architectures, including DenseNet121, VGG16, ResNet50, EfficientNetB0, MobileNet, and InceptionV3, demonstrate the effectiveness and robustness of the proposed framework. The models are evaluated using comprehensive performance metrics such as accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and area under the ROC curve (AUC).
The results show that DenseNet121 achieves the highest accuracy and AUC, while ResNet50 provides the most balanced and reliable predictions with the highest MCC. The findings emphasize that relying solely on accuracy is insufficient for forensic applications, where minimizing false negatives is critical. Overall, the proposed framework improves the visibility of manipulation artifacts and enhances classification robustness, making it suitable for real-world digital image forgery detection scenarios.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.08167 [cs.CV]
(or arXiv:2605.08167v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.08167
arXiv-issued DOI via DataCite
Submission history
From: Gozde Karatas Baydogmus [view email] [v1] Mon, 4 May 2026 17:48:36 UTC (313 KB)
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