Enhancing AI-Generated Image Detection with a Novel Approach and Comparative Analysis

This study explores advancements in AI-generated image detection, emphasizing the increasing realism of images, including deepfakes, and the need for effective detection meth-ods. Traditional Convolutional Neural Networks (CNNs) have shown success but face limitations in generalization and accu-racy, particularly with newer technologies like Diffusion Models. With the evolution of AI image generation models, from CNNs to Generative Adversarial Networks (GANs) and Diffusion Models, detecting synthetic images has become more challenging. Issues include dataset diversity, adversarial attacks, and inconsistencies in pre-processing methods. While state-of-the-art models like CNNs, Vision Transformers (ViTs), and hybrid approaches exist, their accuracy in detecting increasingly sophisticated fake images remains suboptimal. This research proposes a novel hybrid detection model combining CNNs and ViTs with an additional attention mechanism layer. This structure aims to improve the interaction between local and global features, enhancing detection accuracy. The model was trained using the CIFAKE dataset, which contains 120,000 real and AI -generated images. The added attention mechanism enhances feature extraction, addressing limitations in existing models when faced with next-generation synthetic images. The hybrid CNNNiT +Attention model demonstrated improved detection accuracy, achieving 99.77%, surpassing previous methods. This research lays a foundation for stronger AI -generated image detection, helping to mitigate the risks of synthetic image fraud.