Face Presentation Attack Detection

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0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detection
Aug 30, 2026Hatef Otroshi Shahreza, Asif Hussain Khan, Peter Lorenz +2Image Forgery Detection Vision Foundation Model Adaptation
Idiap Research Institute, Switzerland · Université de Lausanne, Switzerland
LoRA-based Adaptation Alone Is Not Enough: Understanding the Limits of Foundation Models for Face Presentation Attack Detection
Aug 10, 2026Peter Lorenz, Anjith George, Marcel SébastienVision Foundation Models Vision Foundation Model Adaptation
Idiap Research Institute, Martigny, Switzerland · University of Lausanne (UNIL), Lausanne, Switzerland
Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark
Jul 29, 2026Peter Lorenz, Anjith George, Sébastien MarcelVision Foundation Models Linear Probing
Idiap Research Institute, Martigny, Switzerland · University of Lausanne (UNIL), Lausanne, Switzerland
FAS-R1: A Unified Multi-Task MLLM for Reasoning Face Anti-Spoofing
Jul 29, 2026Hongyang Wang, Yichen Shi, Hongrui Li +3CoT Reasoning Group Relative Policy Optimization
Shijiazhuang Tiedao University · Fudan University · Shanghai Jiao Tong University +2
DINO-VPT: Hierarchical Visual Prompt Tuning for Joint Physical-Digital Face Anti-Spoofing
Jul 23, 2026Pierre Gallin-Martel, Mika Feng, Koichi Ito +1Vision Foundation Model Adaptation Face Presentation Attack Detection
Graduate School of Information Sciences, Tohoku University, Japan
Unified Face Attack Detection via Fine-Grained Semantic Guidance
Jul 9, 2026Ning Jiang, Shijie Yu, Dingheng Zeng +4Image Forgery Detection Face Presentation Attack Detection
School of Software & Microelectronics, Peking University, Beijing, China · Mashang Consumer Finance Co., Ltd., Chongqing, China
Architectural Bias in Face Presentation Attack Detection: A Comparative Study of Vision Transformers and Convolutional Neural Networks
Jun 16, 2026Ngela Landon Ntung, Floride Tuyisenge, Jema David NdibwileVision Transformer Algorithmic Fairness
College of Engineering, Carnegie Mellon University Africa, Kigali, Rwanda
Flow Augmentation and Knowledge Distillation for Lightweight Face Presentation Attack Detection
May 13, 2026Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim, Taha Hasan Masood Siddique +2Face Presentation Attack Detection
SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia · Interdisciplinary Research Center for Intelligent Secure Systems (IRC-ISS), King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia · College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, China +1
UniShield: Unified Face Attack Detection via KG-Informed Multimodal Reasoning
May 9, 2026Hongrui Li, Yichen Shi, Hongyang Wang +4Multimodal Reasoning Face Presentation Attack Detection
Shijiazhuang Tiedao University · Shanghai Jiao Tong University · Ningbo Institute of Digital Twin +1
Generalized Disguise Makeup Presentation Attack Detection Using an Attention-Guided Patch-Based Framework
Apr 28, 2026Fateme Taraghi, Atefe Aghaei, Mohsen Ebrahimi MoghaddamFace Presentation Attack Detection Face Recognition
Faculty of Computer science and Engineering Shahid Beheshti University, Tehran, Iran
Detection of T-shirt Presentation Attacks in Face Recognition Systems
Apr 21, 2026Mathias Ibsen, Loris Tim Ide, Christian Rathgeb +1Face Presentation Attack Detection Face Recognition
Computer Science, Hochschule Darmstadt, Darmstadt, Germany

Aug 30, 2026, cs.CVJ/K move · Enter open · S save

Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detection

Hatef Otroshi Shahreza, Asif Hussain Khan, Peter Lorenz, Alain Komaty+1

Idiap Research Institute, Switzerland · Université de Lausanne, Switzerland

Image Forgery Detection Vision Foundation Model Adaptation Multimodal Large Language Models Face Presentation Attack Detection Face Recognition Multimodal Model Evaluation

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Face recognition systems are increasingly deployed in security-critical applications, yet they remain vulnerable to presentation and morph attacks. Presentation attack detection (PAD) and morphing attack detection (MAD) are therefore essential components of trustworthy face biometrics. Despite advancements in PAD and MAD methods, existing detectors suffer from limited generalization and degrade in cross-dataset evaluation. In this paper, we systematically investigate whether general-purpose foundation models (FMs) and multimodal large language models (MLLMs) encode PAD-relevant and MAD-relevant information, and how such models can best be deployed for both tasks. We study five approaches with increasing access to the internal information of the model: (i) zero-shot prompting of off-the-shelf MLLMs; (ii) training a shallow model on the next-token logit probabilities at the output of the MLLM; (iii) parameter-efficient fine-tuning on task-specific question-answer data, yielding two specialized MLLMs, called PADLLM and MADLLM, which additionally provide textual reasoning for their decisions; (iv) linear probing of frozen vision encoders; and (v) fine-tuning of vision encoders of FMs and MLLMs. We benchmark 16 open-weight MLLMs and 30 vision encoder backbones on four PAD datasets (MSU-MFSD, CASIA-FASD, Replay-Attack, and OULU-NPU) and four MAD datasets (FFHQ, FRGC, FRLL, and FERET). Our experiments show that FMs and MLLMs can achieve significant performance for PAD and MAD. In addition, the fine-tuned models achieve state-of-the-art detection performance in cross-dataset evaluation, indicating that general-purpose pretrained representations carry substantial attack-relevant information. Source code of all our experiments will be publicly released.

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