The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Can face anti-spoofing countermeasures work in a real world scenario?
Open accessAbstract
User authentication is an important step to protect information and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech equipments. This article assesses how well existing face anti-spoofing countermeasures can work in a more realistic condition. Experiments carried out with two freely available video databases (Replay Attack Database and CASIA Face Anti-Spoofing Database) show low generalization and possible database bias in the evaluated countermeasures. To generalize and deal with the diversity of attacks in a real world scenario we introduce two strategies that show promising results.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
Kuncheva, L. I., & Whitaker, C. J. (2003) · Machine Learning · doi:10.1023/a:1022859003006 · Cited by 2,335
(2008) · doi:10.1007/978-0-387-71041-9 · Cited by 1,406
Cited by
Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8682602 · Cited by 951
Face anti-spoofing using patch and depth-based CNNs
Atoum, Y., Liu, Y., Jourabloo, A., & Liu, X. (2017) · 2017 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2017.8272713 · Cited by 429
Deep Tree Learning for Zero-Shot Face Anti-Spoofing
Liu, Y., Stehouwer, J., Jourabloo, A., & Liu, X. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00481 · Cited by 278
Deep learning for face anti-spoofing: A survey
Yu, Z., Qin, Y., Li, X., Zhao, C., Lei, Z., & Zhao, G. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3215850 · Cited by 227
SWAPPED! Digital face presentation attack detection via weighted local magnitude pattern
Agarwal, A., Singh, R., Vatsa, M., & Noore, A. (2017) · 2017 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2017.8272754 · Cited by 85
Metadata
- Authors
- de Freitas Pereira, T., Anjos, A., De Martino, J. M., & Marcel, S.
- Venue
- 2013 International Conference on Biometrics (ICB)
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 1-8
- OpenAlex
- W2106852298
- Cited by
- 271
- References
- 2
- FWCI
- 12.52
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Can face anti-spoofing countermeasures work in a real world scenario? https://app.cochat.ai/papers/10.1109/icb.2013.6612981
- The paper
- de Freitas Pereira, T., Anjos, A., De Martino, J. M., & Marcel, S. (2013). Can face anti-spoofing countermeasures work in a real world scenario? 2013 International Conference on Biometrics (ICB). https://doi.org/10.1109/icb.2013.6612981
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
