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

arXiv: https://arxiv.org/abs/2603.15050 · HTML: https://mlsanity.com/paper/2603.15050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors](https://mlsanity.com/paper/2203.06691/) (2022)
- [MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders](https://mlsanity.com/paper/2302.01843/) (2023)
- [Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks](https://mlsanity.com/paper/2012.05344/) (2020)
- [SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2208.07337/) (2022)
- [PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack Detection](https://mlsanity.com/paper/2108.10291/) (2021)
- [Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detection](https://mlsanity.com/paper/2208.05787/) (2022)
- [MADation: Face Morphing Attack Detection with Foundation Models](https://mlsanity.com/paper/2501.03800/) (2025)
- [Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from Vision Transformer](https://mlsanity.com/paper/2501.09817/) (2025)
- [Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2306.15733/) (2023)
- [OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement](https://mlsanity.com/paper/2208.07841/) (2022)
- [Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection](https://mlsanity.com/paper/2306.03002/) (2023)
- [Face Feature Visualisation of Single Morphing Attack Detection](https://mlsanity.com/paper/2304.13021/) (2023)
- [SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning](https://mlsanity.com/paper/2504.05504/) (2025)
- [Evaluating the Effectiveness of Attack-Agnostic Features for Morphing Attack Detection](https://mlsanity.com/paper/2410.16802/) (2024)
- [FD-MAD: Frequency-Domain Residual Analysis for Face Morphing Attack Detection](https://mlsanity.com/paper/2601.20656/) (2026)

## Abstract (arXiv)

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.