Comparative Evaluation of Deep Learning Models for Fake Image Detection

• URL: http://arxiv.org/abs/2605.20971v1
• Date: Wed, 20 May 2026 09:57:58 GMT
• Title: Comparative Evaluation of Deep Learning Models for Fake Image Detection
• Abstract summary: This study compares the performance of four CNN architectures for fake image detection using a unified preprocessing and training pipeline.<n>VGG16 achieved the highest accuracy at 91%, with XceptionNet, ResNet50, and EfficientNetB0 each reaching 90%.<n>The study underscores the need for balanced datasets, advanced augmentation, and fairness-aware training to develop reliable fake image detection systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing sophistication of GAN-based image manipulation presents significant challenges for digital forensics. This study compares the performance of four pretrained CNN architectures including VGG16, ResNet50, EfficientNetB0, and XceptionNet for fake image detection using a unified preprocessing and training pipeline. A dataset of real and manipulated images was processed through resizing, normalization, and augmentation to address class imbalance and improve generalization. Models were evaluated using Accuracy, Precision, Recall, F1-score, and ROC-AUC. VGG16 achieved the highest accuracy at 91%, with XceptionNet, ResNet50, and EfficientNetB0 each reaching 90%. EfficientNetB0 showed stronger sensitivity to fake images but reduced reliability on real samples, reflecting imbalance-driven bias. Limitations include dataset imbalance, overfitting, and limited interpretability, which affect cross-domain robustness. The study provides a reproducible baseline and underscores the need for balanced datasets, advanced augmentation, and fairness-aware training to develop reliable fake image detection systems.

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