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

Summary

This paper presents R-FLoRA, a single-image face morphing attack detection (S-MAD) framework that combines high-frequency Laplacian residual statistics with a frozen, foundation-scale vision transformer. It uses residual-statistic-gated low-rank adapters, feature-wise residual fusion, and a residual-contrastive alignment loss. Tested on four ICAO-compliant datasets covering seven morph generation techniques, it outperforms nine recent S-MAD algorithms in accuracy and cross-dataset generalisation while running in real time.

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