Survey Paper: Comparative Analysis of Detection Methods for Real vs. AI-Generated Images

The rapid evolution of AI-generated image technologies, including GANs and diffusion models, has blurred the lines between real and synthetic content. In this survey, we explore state-of-the-art methods for detecting fake images. Traditional forensic techniques, deep learning approaches, and advanced methods like Explainable AI (XAI) and blockchain-based verification are used. We evaluate these methods using performance metrics and derive insights into their strengths and limitations. A comparative analysis of existing research papers is provided, examining the approaches used, feature extraction techniques, classifiers employed, and datasets utilized. Additionally, it puts forth actional research directions like diffusion models, multimodal analysis, and expansion of dataset that can contribute towards the advancement of this field. Practical examples and visualizations make this survey both insightful and tutorial-like, offering valuable guidance for researchers and practitioners.