Detecting AI-generated images remains a formidable challenge due to the difficulty of generalizing across novel generative models and paradigms. This generalization gap mainly stems from overfitting to semantic content and model-specific patterns. Moreover, many state-of-the-art detectors employ complex architectures and heavy computational procedures, limiting their practicality in real-world deployments. We propose RealNet, a novel unsupervised framework that learns a disentangled, forgery-aware representation space solely from real images, mitigating overfitting to both semantic and model-specific information. Our approach extracts semantic-agnostic representations via a dual adversarial denoising mechanism, yielding compact, low intra-class variance features. These are perturbed in feature space to produce pseudo-negative samples for training a lightweight discriminator, enabling robust detection without dependence on fake samples. Extensive evaluation across diverse generative paradigms, including an expanded benchmark of state-of-the-art VAR-based models, demonstrates RealNet’s superior generalization capabilities and robustness. It delivers remarkable 4.51\% and 3.93\% average improvements in accuracy and average precision over current state-of-the-art methods, all while incurring low computational cost through its lightweight and unsupervised design. Additionally, we introduce a medically-relevant forged image dataset, confirming RealNet’s effectiveness in high-stakes, domain-shifted scenarios. These advantages make RealNet a practical and scalable solution for AI-generated image detection with strong potential for real-world and social impact.