Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting

TL;DR

This paper introduces a fusion-based few-shot learning approach for face morphing attack detection and fingerprinting, enabling better generalization to unseen attack types and providing a large-scale benchmark database.

Contribution

It extends MAD from supervised to few-shot learning and from binary detection to multiclass fingerprinting using fusion of PRNU and Noiseprint models.

Findings

Outperforms existing MAD methods in generalization to unseen attacks

Achieves high accuracy in multiclass morphing attack fingerprinting

Provides a large-scale, diverse database for benchmarking

Abstract

The vulnerability of face recognition systems to morphing attacks has posed a serious security threat due to the wide adoption of face biometrics in the real world. Most existing morphing attack detection (MAD) methods require a large amount of training data and have only been tested on a few predefined attack models. The lack of good generalization properties, especially in view of the growing interest in developing novel morphing attacks, is a critical limitation with existing MAD research. To address this issue, we propose to extend MAD from supervised learning to few-shot learning and from binary detection to multiclass fingerprinting in this paper. Our technical contributions include: 1) We propose a fusion-based few-shot learning (FSL) method to learn discriminative features that can generalize to unseen morphing attack types from predefined presentation attacks; 2) The proposed FSL based on the fusion of the PRNU model and Noiseprint network is extended from binary MAD to multiclass morphing attack fingerprinting (MAF). 3) We have collected a large-scale database, which contains five face datasets and eight different morphing algorithms, to benchmark the proposed few-shot MAF (FS-MAF) method. Extensive experimental results show the outstanding performance of our fusion-based FS-MAF. The code and data will be publicly available at https://github.com/nz0001na/mad maf.