SRL-MAD: Structured Residual Latents for One-Class Morphing Attack Detection

TL;DR

SRL-MAD introduces a novel frequency-aware, one-class face morphing attack detection method that leverages structured residual Fourier representations to effectively identify unseen attacks without relying on attack-labeled data.

Contribution

The paper proposes SRL-MAD, a new one-class MAD approach using structured residual Fourier features and frequency-informed inductive bias for improved open-set attack detection.

Findings

Outperforms recent one-class and supervised MAD models on multiple datasets.

Uses frequency-aware spectral projections as a discriminative feature representation.

Avoids reliance on reconstruction errors by mapping features directly into a scoring space.

Abstract

Face morphing attacks represent a significant threat to biometric systems as they allow multiple identities to be combined into a single face. While supervised morphing attack detection (MAD) methods have shown promising performance, their reliance on attack-labeled data limits generalization to unseen morphing attacks. This has motivated increasing interest in one-class MAD, where models are trained exclusively on bona fide samples and are expected to detect unseen attacks as deviations from the normal facial structure. In this context, we introduce SRL-MAD, a one-class single-image MAD that uses structured residual Fourier representations for open-set morphing attack detection. Starting from a residual frequency map that suppresses image-specific spectral trends, we preserve the two-dimensional organization of the Fourier domain through a ring-based representation and replace azimuthal averaging with a learnable ring-wise spectral projection. To further encode domain knowledge about where morphing artifacts arise, we impose a frequency-informed inductive bias by organizing spectral evidence into low, mid, and high-frequency bands and learning cross-band interactions. These structured spectral features are mapped into a latent space designed for direct scoring, avoiding the reliance on reconstruction errors. Extensive evaluation on FERET-Morph, FRLL-Morph, and MorDIFF demonstrates that SRL-MAD consistently outperforms recent one-class and supervised MAD models. Overall, our results show that learning frequency-aware projections provides a more discriminative alternative to azimuthal spectral summarization for one-class morphing attack detection.