SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data

• URL: http://arxiv.org/abs/2303.02660v2
• Date: Tue, 11 Apr 2023 09:38:16 GMT
• Title: SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data
• Authors: Meiling Fang and Marco Huber and Naser Damer
• Abstract summary: This work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. We show the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data.
• Score: 6.218678900574128
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legally-sensitive authentic biometric data with a limited number of subjects. To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The bona fide samples in SynthASpoof are synthetically generated and the attack samples are collected by presenting such synthetic data to capture systems in a real attack scenario. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. Moreover, we boost the performance of such a solution by incorporating the domain generalization tool MixStyle into the PAD solutions. Additionally, we showed the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data and consistently enhance PAD performances. The SynthASpoof dataset, containing 25,000 bona fide and 78,800 attack samples, the implementation, and the pre-trained weights are made publicly available.

Related papers

• SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data [44.304022773272415]
  We introduce SynFER, a novel framework for synthesizing facial expression image data based on high-level textual descriptions. We propose a semantic guidance technique to steer the generation process and a pseudo-label generator to help rectify the facial expression labels. Our approach achieves a 67.23% classification accuracy on AffectNet when training solely with synthetic data equivalent to the AffectNet training set size.
  arXiv  Detail & Related papers  (2024-10-13T14:58:21Z)
• Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data [104.45155847778584]
  This paper presents an overview of the 2nd edition of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) FRCSyn aims to investigate the use of synthetic data in face recognition to address current technological limitations.
  arXiv  Detail & Related papers  (2024-04-16T08:15:10Z)
• SDFR: Synthetic Data for Face Recognition Competition [51.9134406629509]
  Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. Recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones.
  arXiv  Detail & Related papers  (2024-04-06T10:30:31Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• Statistical properties and privacy guarantees of an original distance-based fully synthetic data generation method [0.0]
  This work shows the technical feasibility of generating publicly releasable synthetic data using a multi-step framework. By successfully assessing the quality of data produced using a novel multi-step synthetic data generation framework, we showed the technical and conceptual soundness of the Open-CESP initiative.
  arXiv  Detail & Related papers  (2023-10-10T12:29:57Z)
• Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data [9.061271587514215]
  We show how this assumption can be removed, allowing for MIAs to be performed using only the synthetic data. Our results show that MIAs are still successful, across two real-world datasets and two synthetic data generators.
  arXiv  Detail & Related papers  (2023-07-04T13:16:03Z)
• Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results [73.98594459933008]
  Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets. We introduce the Wild Face Anti-Spoofing dataset, a large-scale, diverse FAS dataset collected in unconstrained settings.
  arXiv  Detail & Related papers  (2023-04-12T10:29:42Z)
• Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets [83.749895930242]
  We propose two techniques for producing high-quality naturalistic synthetic occluded faces. We empirically show the effectiveness and robustness of both methods, even for unseen occlusions. We present two high-resolution real-world occluded face datasets with fine-grained annotations, RealOcc and RealOcc-Wild.
  arXiv  Detail & Related papers  (2022-05-12T17:03:57Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)
• On the use of automatically generated synthetic image datasets for benchmarking face recognition [2.0196229393131726]
  Recent advances in Generative Adversarial Networks (GANs) provide a pathway to replace real datasets by synthetic datasets. Recent advances in Generative Adversarial Networks (GANs) to synthesize realistic face images provide a pathway to replace real datasets by synthetic datasets. benchmarking results on the synthetic dataset are a good substitution, often providing error rates and system ranking similar to the benchmarking on the real dataset.
  arXiv  Detail & Related papers  (2021-06-08T09:54:02Z)
• Holdout-Based Fidelity and Privacy Assessment of Mixed-Type Synthetic Data [0.0]
  AI-based data synthesis has seen rapid progress over the last several years, and is increasingly recognized for its promise to enable privacy-respecting data sharing. We introduce and demonstrate a holdout-based empirical assessment framework for quantifying the fidelity as well as the privacy risk of synthetic data solutions.
  arXiv  Detail & Related papers  (2021-04-01T17:30:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.