Optimizing Cnn Performance for Ai-generated Image Classification: a Comparative Study of Architectures and Optimizers Using K-fold Cross-validation
Year: 2025
Type: article
Abstract: This study investigates CNN optimization for classifying AI-generated images. Using the CIFAKE dataset (60,000 real and 60,000 AI-generated images), we evaluated four CNN configurations with varying complexity and four optimization algorithms through 5-fold cross-validation. Our findings show Configuration 4 (4 Conv, 2 MaxPool) with Adam optimizer achieved the highest validation accuracy (0.8368±0.0135). Adam demonstrated consistent performance across architectures, while SGD showed strong but variable results improving with model complexity. Adagrad and Adadelta consistently underperformed. The final model achieved 85.28% test accuracy with balanced precision (0.8531) and recall (0.8528). Results indicate more complex architectures combined with adaptive optimizers like Adam provide superior performance for AI-generated image classification, with the balance between model complexity and optimizer selection being crucial. The consistent performance across real and fake categories demonstrates this approach's robustness for deepfake detection applications.
Author: Fransiscus Rolanda Malau
Institution: Universitas Nusa Mandiri
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409900971