arXiv Machine Learning July 30, 2026 By Peter Lorenz, Anjith George, S\'ebastien Marcel

Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark

Read the original on arXiv Machine Learning →

arXiv:2607. 26993v1 Announce Type: new Abstract: Face presentation attack detection (PAD) remains challenging under cross-dataset evaluation, where domain shift degrades models trained on a single dataset.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 11

LoRA-based Adaptation Alone Is Not Enough: Understanding the Limits of Foundation Models for Face Presentation Attack Detection

arXiv:2608. 09633v1 Announce Type: cross Abstract: Face presentation attack detection (PAD) aims to reliably detect a wide range of presentation attacks.

By Peter Lorenz, Anjith George, Marcel S\'ebastien

llms fine-tuning benchmarks

arXiv Computer Vision

Sep 1

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

arXiv:2608.29802v1 Announce Type: new Abstract: Face recognition systems are increasingly deployed in security-critical applications, yet they remain vulnerable to presentation and morph attacks. Pre...

By Hatef Otroshi Shahreza, Asif Hussain Khan, Peter Lorenz, Alain Komaty, S\'ebastien Marcel

llms fine-tuning multimodal benchmarks

arXiv Computer Vision

Aug 25

Tomatoes, Potatoes, and Onions: Questioning the Need for Faces in Face Presentation Attack Detection

arXiv:2608.21455v1 Announce Type: new Abstract: Face presentation attack detection (PAD) is traditionally formulated as a face-specific problem, although many of the visual artifacts introduced by pr...

By Guray Ozgur, Fadi Boutros, Naser Damer

benchmarks safety

arXiv AI

Jul 3

CPG-PAD: Concept-Informed Prompts Guided Presentation Attack Detection

arXiv:2607. 01303v1 Announce Type: cross Abstract: Presentation Attack Detection (PAD) serves as a crucial safeguard for face recognition systems against presentation attacks such as printed photos, replayed videos, and 3D masks.

By Haoyuan Zhang, Xiangyu Zhu, Li Gao, Ajian Liu, Siran Peng, Zhen Lei

llms multimodal benchmarks

arXiv AI

Aug 18

Steering the Flow: Inverting Face Recognition Models via Gradient-Guided Flow Matching

arXiv:2608. 16791v1 Announce Type: cross Abstract: Model Inversion Attacks (MIAs) aim to reconstruct representative training samples of target identities from face recognition models, exposing critical security vulnerabilities.

By Ye Lu, Shen Wang, Zhaoyang Zhang, Yihan Yan, Li Liu, Runze Liu, Fanghui Sun

diffusion benchmarks

Hugging Face Trending Papers

Aug 17

Steering the Flow: Inverting Face Recognition Models via Gradient-Guided Flow Matching

Model Inversion Attacks (MIAs) aim to reconstruct representative training samples of target identities from face recognition models, exposing critical security vulnerabilities. Existing methods typically rely on indirect guidance or highly stochastic guidance, making it difficult to stably optimize generation trajectories toward target facial images.

diffusion benchmarks

One email a morning, machine-written

Related stories

LoRA-based Adaptation Alone Is Not Enough: Understanding the Limits of Foundation Models for Face Presentation Attack Detection

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

Tomatoes, Potatoes, and Onions: Questioning the Need for Faces in Face Presentation Attack Detection

CPG-PAD: Concept-Informed Prompts Guided Presentation Attack Detection

Steering the Flow: Inverting Face Recognition Models via Gradient-Guided Flow Matching

Steering the Flow: Inverting Face Recognition Models via Gradient-Guided Flow Matching