56 research outputs found
Face morphing detection in the presence of printing/scanning and heterogeneous image sources
Face morphing represents nowadays a big security threat in the context of
electronic identity documents as well as an interesting challenge for
researchers in the field of face recognition. Despite of the good performance
obtained by state-of-the-art approaches on digital images, no satisfactory
solutions have been identified so far to deal with cross-database testing and
printed-scanned images (typically used in many countries for document issuing).
In this work, novel approaches are proposed to train Deep Neural Networks for
morphing detection: in particular generation of simulated printed-scanned
images together with other data augmentation strategies and pre-training on
large face recognition datasets, allowed to reach state-of-the-art accuracy on
challenging datasets from heterogeneous image sources
Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting
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
Artificial Image Tampering Distorts Spatial Distribution of Texture Landmarks and Quality Characteristics
Advances in AI based computer vision has led to a significant growth in
synthetic image generation and artificial image tampering with serious
implications for unethical exploitations that undermine person identification
and could make render AI predictions less explainable.Morphing, Deepfake and
other artificial generation of face photographs undermine the reliability of
face biometrics authentication using different electronic ID documents.Morphed
face photographs on e-passports can fool automated border control systems and
human guards.This paper extends our previous work on using the persistent
homology (PH) of texture landmarks to detect morphing attacks.We demonstrate
that artificial image tampering distorts the spatial distribution of texture
landmarks (i.e. their PH) as well as that of a set of image quality
characteristics.We shall demonstrate that the tamper caused distortion of these
two slim feature vectors provide significant potentials for building
explainable (Handcrafted) tamper detectors with low error rates and suitable
for implementation on constrained devices.Comment: 6 pages, 7 figures, 3 table
A Double Siamese Framework for Differential Morphing Attack Detection
Face morphing and related morphing attacks have emerged as a serious security threat for automatic face recognition systems and a challenging research field. Therefore, the availability of effective and reliable morphing attack detectors is strongly needed. In this paper, we proposed a framework based on a double Siamese architecture to tackle the morphing attack detection task in the differential scenario, in which two images, a trusted live acquired image and a probe image (morphed or bona fide) are given as the input for the system. In particular, the presented framework aimed to merge the information computed by two different modules to predict the final score. The first one was designed to extract information about the identity of the input faces, while the second module was focused on the detection of artifacts related to the morphing process. Experimental results were obtained through several and rigorous cross-dataset tests, exploiting three well-known datasets, namely PMDB, MorphDB, and AMSL, containing automatic and manually refined facial morphed images, showing that the proposed framework was able to achieve satisfying results
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