Light2Lie: Detecting Deepfake Images Using Physical Reflectance Laws

The rapid advancement of generative models such as GANs and diffusion-based architectures has led to the widespread creation of hyperrealistic synthetic images. Although these technologies drive innovation in the media and data generation, they also raise significant ethical, social, and security concerns. In response, numerous detection methods have been developed, including frequency domain analysis and deep learning classifiers. However, these approaches often struggle to generalize across unseen generative models and typically lack physical grounding, leaving them vulnerable to adaptive attacks and limited in interpretability. We propose Light2Lie, a physics-augmented deepfake detection framework that leverages principles of specular reflection, specifically the Fresnel reflectance model, to reveal inconsistencies in light–surface interactions that generative models struggle to reproduce effectively. Our method first employs a neural network to estimate the surface base reflectance and then derives a microfacet-inspired specular response map that encodes subtle geometric and optical discrepancies between real and synthetic images. This signal is integrated into a secondary classifier, as feature maps, that learns to distinguish the two classes based on reflectance-driven patterns. To further enhance robustness, we introduce a feedback refinement mechanism that updates the base reflectance model output using classification errors, tightly coupling physical modeling with the learning objective. Extensive experiments on multiple deepfake datasets demonstrate that our approach obtains better generalization performance to unseen generative model samples by getting up to 74% precision on diverse deepfake domains, outperforming state-of-the-art baselines while providing robust, physics-grounded decisions.