Detection of AI-Generated Images Using Pretrained CNN Feature Extraction and Support Vector Machine Classification

Abstract

Because Generative AI, we now see highly realistic synthetic images that are difficult to distinguish from the real thing. This technology raises concerns about misinformation, the trustworthiness of digital content, and how people can misuse synthetic media. Therefore, finding an accurate and efficient way to detect AI-Generated images is a significant research challenge. In this research, we explore a hybrid image classification setup: we combine CNN-based feature extraction (using a pre-trained network) with SVM classification to determine which images are AI-Generated and which are real. For feature extraction, we use MobileNetV2, pre-trained on ImageNet, as the fixed extractor. Then, to classify these features, we use SVM with an RBF kernel. This experiment uses the Kaggle dataset AI Generated Images vs real Images, with a total of 975 images divided into training and testing sets with a stratified 80:20 split. Our model performs quite well: 84.1% accuracy, 81.8% precision, 91.7% recall, and an F1 score of 86.5%. The confusion matrix shows that the model is able to detect most of the AI-generated images, demonstrating its sensitivity to synthetic content. Overall, our CNN–SVM setup offers an effective and efficient alternative to full end-to-end deep learning methods for detecting these images. This holds promise for applications such as digital content verification, combating disinformation, and automated authenticity checking.