RealNet: Efficient and Unsupervised Detection of AI-Generated Images via Real-Only Representation Learning

Shuaibo Li, Laixin Zhang, Wei Ma, Jianwei Guo, Shibiao Xu, Zhijie Qiu, Hongbin Zha

Abstract:

Detecting AI-generated images remains a persistent challenge, as existing detectors often struggle to generalize to forgeries produced by previously unseen generative models. This generalization gap mainly stems from entanglement with semantic content and overfitting to modelspecific artifacts. Moreover, many state-of-the-art methods rely on large pre-trained backbones or computationally intensive pipelines, which limit their applicability in real-world, resource-constrained environments. We propose RealNet, a
lightweight and unsupervised framework that constructs a disentangled, forgery-aware representation space using only real images. RealNet first extracts semantic-agnostic representations through a dual adversarial denoising mechanism, producing compact features with low intra-class variance.
These representations are then perturbed in feature space to generate pseudo-negative samples, which are combined with the original real features to train a lightweight discriminator, enabling robust detection without any dependence on synthetic images during training. Comprehensive evaluations across GAN, diffusion, and emerging VAR-based paradigms demonstrate that RealNet achieves superior cross-model generalization and robustness. RealNet surpasses previous stateof-the-art approaches by 4.51% in accuracy and 3.93% in average precision, while maintaining significantly lower computational cost. Furthermore, we introduce a medically relevant synthetic image dataset and show RealNet remains effective under severe distribution shifts, highlighting its potential for deployment in high-stakes real-world scenarios. Together, these advantages position RealNet as a practical, scalable and socially impactful solution for robust AI-generated image detection.