CNN and Transfer-Learning Methods for Fake Image Detection

The advent of AI-generated and manipulated images has increased the necessity for effective detection systems.Convolutional neural networks (CNNs) are the leader in fake image classification, but their performance is dependent on training methodology.This paper contrasts CNNs trained from scratch with transfer learning (TL) for fake image detection.We compare models across benchmarks and manipulation types with focus on critical metrics: classification accuracy, computational efficiency (training time and model size), generalization to new data, and resource utilization (memory/GPU).For example, a recent work reported that a MobileNet model fine-tuned using transfer learning reached 90.10% accuracy on the CIFAKE AI-generated image dataset, outperforming larger architectures.We present experimental results in tables and figures to highlight performance trends.We examine the effect of data quality and manipulation complexity (e.g., StyleGAN vs. human-edited images) on detection.Our results show that transfer learning tends to provide higher accuracy and faster convergence with limited data, while scratch-trained networks may require additional data and compute to reach similar performance.However, dataset biases (e.g.background cues) may affect all models.We conclude with recommendations on selecting appropriate models given hardware constraints and application requirements.