R-FLoRA: Residual-Statistic-Gated Low-Rank Adaptation for Single-Image Face Morphing Attack Detection

Face morphing attacks (blending two faces together to fool facial recognition systems) threaten security systems used at borders and for digital identity checks, and detecting them from a single image is difficult because there's no trusted reference image to compare against. This paper presents R-FLoRA, a new detection method that combines high-frequency image analysis (looking at fine details) with a frozen, large-scale vision transformer (a type of AI model trained on images) to spot morphing artifacts while keeping the overall understanding of the face intact. The method outperforms nine other detection approaches on multiple test datasets and works efficiently in real-world biometric verification systems.