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

• URL: http://arxiv.org/abs/2604.17321v1
• Date: Sun, 19 Apr 2026 08:21:45 GMT
• Title: R-FLoRA: Residual-Statistic-Gated Low-Rank Adaptation for Single-Image Face Morphing Attack Detection
• Abstract summary: Face morphing attacks pose a substantial risk to the reliability of face recognition systems.<n>This paper presents a new Single-Image Face Morphing Attack Detection framework.<n>It integrates high-frequency Laplacian residual statistics with representations from a frozen, foundation-scale vision transformer.
• Score: 6.348134327816868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face morphing attacks pose a substantial risk to the reliability of face recognition systems used in passport issuance, border control, and digital identity verification. Detecting morphing attacks from a single facial image remains challenging owing to the lack of a trusted reference and the diversity of attack generation methods. This paper presents a new Single-Image Face Morphing Attack Detection (S-MAD) framework that integrates high-frequency Laplacian residual statistics with representations from a frozen, foundation-scale vision transformer. The approach employs residual-statistic-gated low-rank adapters (R-FLoRA) and feature-wise residual fusion (Res-FiLM) to enhance sensitivity to local morphing artefacts while preserving the semantic context of the backbone. A novel residual-contrastive alignment loss further regularises the fused token space, improving discrimination under unseen morphing conditions. Comprehensive experiments on four ICAO-compliant datasets, encompassing seven morph generation techniques, demonstrate that the proposed method consistently surpasses nine recent state-of-the-art S-MAD algorithms in detection accuracy and cross-domain (or dataset) generalisation. With a frozen backbone and minimal trainable parameters, the model achieves real-time efficiency and interpretability, making it suitable for real-life scenarios in biometric verification systems.

Related papers

• Face-D(^2)CL: Multi-Domain Synergistic Representation with Dual Continual Learning for Facial DeepFake Detection [21.09692782202372]
  Continual learning provides an effective approach to adapt facial DeepFake detection models to evolving forgery patterns.<n>We propose Face-D(2)CL, a framework for facial DeepFake detection.
  arXiv  Detail & Related papers  (2026-04-09T12:18:42Z)
• FD-MAD: Frequency-Domain Residual Analysis for Face Morphing Attack Detection [5.931399156681511]
  We introduce a region-aware frequency-based morph detection strategy that drastically improves over strong baseline methods.<n>Our approach achieves an average equal error rate (EER) of 1.85% on FRLL-Morph and ranks second on MAD22 with an average EER of 6.12%.
  arXiv  Detail & Related papers  (2026-01-28T14:38:51Z)
• LoRA-Enhanced Vision Transformer for Single Image based Morphing Attack Detection via Knowledge Distillation from EfficientNet [7.409512128477373]
  We propose a novel Single-Image Morphing Attack Detection (S-MAD) approach using a teacher-student framework.<n>To improve efficiency, we integrate Low-Rank Adaptation (LoRA) for fine-tuning, reducing computational costs while maintaining high detection accuracy.<n>The proposed method is benchmarked against six state-of-the-art S-MAD techniques, demonstrating superior detection performance and computational efficiency.
  arXiv  Detail & Related papers  (2025-11-16T13:58:11Z)
• Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from Vision Transformer [13.21801650767302]
  Face morphing attacks have posed severe threats to Face Recognition Systems (FRS), which are operated in border control and passport issuance use cases.<n>We propose a generalized single-image-based MAD (S-MAD) algorithm by learning the encoding from Vision Transformer (ViT) architecture.<n>Experiments are carried out on face morphing datasets generated using publicly available FRGC face datasets.
  arXiv  Detail & Related papers  (2025-01-16T20:09:19Z)
• Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture [81.93945602120453]
  We introduce an approach that is both general and parameter-efficient for face forgery detection.<n>We design a forgery-style mixture formulation that augments the diversity of forgery source domains.<n>We show that the designed model achieves state-of-the-art generalizability with significantly reduced trainable parameters.
  arXiv  Detail & Related papers  (2024-08-23T01:53:36Z)
• UniForensics: Face Forgery Detection via General Facial Representation [60.5421627990707]
  High-level semantic features are less susceptible to perturbations and not limited to forgery-specific artifacts, thus having stronger generalization. We introduce UniForensics, a novel deepfake detection framework that leverages a transformer-based video network, with a meta-functional face classification for enriched facial representation.
  arXiv  Detail & Related papers  (2024-07-26T20:51:54Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection [81.59191603867586]
  Sequential deepfake detection aims to identify forged facial regions with the correct sequence for recovery. The recovery of forged images requires knowledge of the manipulation model to implement inverse transformations. We propose Multi-Collaboration and Multi-Supervision Network (MMNet) that handles various spatial scales and sequential permutations in forged face images.
  arXiv  Detail & Related papers  (2023-07-06T02:32:08Z)
• Reliable Face Morphing Attack Detection in On-The-Fly Border Control Scenario with Variation in Image Resolution and Capture Distance [3.6833521970861685]
  Face morphing attacks are highly potential in deceiving automatic FRS and human observers. We present a novel Differential-MAD (D-MAD) algorithm based on the spherical distances and hierarchical fusion of deep features. Experiments are carried out on the newly generated face morphing dataset (SCFace-Morph) based on the publicly available SCFace dataset.
  arXiv  Detail & Related papers  (2022-09-30T13:58:43Z)
• Asymmetric Modality Translation For Face Presentation Attack Detection [55.09300842243827]
  Face presentation attack detection (PAD) is an essential measure to protect face recognition systems from being spoofed by malicious users. We propose a novel framework based on asymmetric modality translation forPAD in bi-modality scenarios. Our method achieves state-of-the-art performance under different evaluation protocols.
  arXiv  Detail & Related papers  (2021-10-18T08:59:09Z)
• Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario [50.36920272392624]
  Face anti-spoofing is the key to preventing security breaches in biometric recognition applications. Deep learning method using RGB and infrared images demands a large amount of training data for new attacks. We present a face anti-spoofing method in a real-world scenario by automatic learning the physical characteristics in polarization images of a real face.
  arXiv  Detail & Related papers  (2020-03-18T03:04:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.