Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting
- URL: http://arxiv.org/abs/2210.15510v1
- Date: Thu, 27 Oct 2022 14:46:53 GMT
- Title: Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting
- Authors: Na Zhang, Shan Jia, Siwei Lyu, and Xin Li
- Abstract summary: Face recognition systems are vulnerable to morphing attacks.
Most existing morphing attack detection methods require a large amount of training data and have only been tested on a few predefined attack models.
We propose to extend MAD from supervised learning to few-shot learning and from binary detection to multiclass fingerprinting.
- Score: 37.161842673434705
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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