Name
Abstract | ||
|---|---|---|
In the last few decades, deep-learning-based face verification and recognition systems have had enormous success in solving complex security problems. However, it has been recently shown that such efficient frameworks are vulnerable to face-spoofing attacks, which has led researchers to build proficient anti-facial-spoofing (or liveness detection) models as an additional security layer. In response, increasingly challenging and tricky attacks have been launched to fool these anti-spoofing mechanisms. In this context, this paper presents the results of an analytical study on transfer-learning-based convolutional neural networks (CNNs) for face liveness detection and differential evolution-based adversarial attacks to evaluate the efficiency of face anti-spoofing classifiers against adversarial attacks. Specifically, experiments were conducted under different use-case scenarios on four face anti-spoofing databases to highlight practical criteria that can be used in the development of countermeasures to address face-spoofing issues. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11042-020-10041-1 | MULTIMEDIA TOOLS AND APPLICATIONS |
Keywords | DocType | Volume |
Face liveness detection, Spoofing attacks, Convolutional neural networks, Differential evolution, Deep learning | Journal | 80 |
Issue | ISSN | Citations |
5 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naima Bousnina | 1 | 0 | 0.34 |
| Lilei Zheng | 2 | 0 | 0.34 |
| Mounia Mikram | 3 | 11 | 4.00 |
| Sanaa Ghouzali | 4 | 77 | 12.30 |
| Khalid Minaoui | 5 | 7 | 7.42 |