Unknown presentation attack detection against rational attackers
Abstract Despite the impressive progress in the field of presentation attack detection and multimedia forensics over the last decade, these systems are still vulnerable to attacks in real‐life settings. Some of the challenges for the existing solutions are the detection of unknown attacks, the abili...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-09-01
|
| Series: | IET Biometrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/bme2.12053 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546739391823872 |
|---|---|
| author | Ali Khodabakhsh Zahid Akhtar |
| author_facet | Ali Khodabakhsh Zahid Akhtar |
| author_sort | Ali Khodabakhsh |
| collection | DOAJ |
| description | Abstract Despite the impressive progress in the field of presentation attack detection and multimedia forensics over the last decade, these systems are still vulnerable to attacks in real‐life settings. Some of the challenges for the existing solutions are the detection of unknown attacks, the ability to perform in adversarial settings, few‐shot learning, and explainability. In this study, these limitations are approached by reliance on a game‐theoretic view for modelling the interactions between the attacker and the detector. Consequently, a new optimisation criterion is proposed and a set of requirements are defined for improving the performance of these systems in real‐life settings. Furthermore, a novel detection technique is proposed using generator‐based feature sets that are not biased towards any specific attack species. To further optimise the performance on known attacks, a new loss function coined categorical margin maximisation loss (C‐marmax) is proposed, which gradually improves the performance against the most powerful attack. The proposed approach provides a more balanced performance across known and unknown attacks and achieves state‐of‐the‐art performance in known and unknown attack detection cases against rational attackers. Lastly, the few‐shot learning potential of the proposed approach as well as its ability to provide pixel‐level explainability is studied. |
| format | Article |
| id | doaj-art-7920fee995f14b609e2c6880a619c555 |
| institution | Kabale University |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-7920fee995f14b609e2c6880a619c5552025-02-03T06:47:18ZengWileyIET Biometrics2047-49382047-49462021-09-0110546047910.1049/bme2.12053Unknown presentation attack detection against rational attackersAli Khodabakhsh0Zahid Akhtar1Department of Information Security and Communication Technology Norwegian University of Science and Technology Gjøvik NorwayDepartment of Network and Computer Security State University of New York Polytechnic Institute Utica New York USAAbstract Despite the impressive progress in the field of presentation attack detection and multimedia forensics over the last decade, these systems are still vulnerable to attacks in real‐life settings. Some of the challenges for the existing solutions are the detection of unknown attacks, the ability to perform in adversarial settings, few‐shot learning, and explainability. In this study, these limitations are approached by reliance on a game‐theoretic view for modelling the interactions between the attacker and the detector. Consequently, a new optimisation criterion is proposed and a set of requirements are defined for improving the performance of these systems in real‐life settings. Furthermore, a novel detection technique is proposed using generator‐based feature sets that are not biased towards any specific attack species. To further optimise the performance on known attacks, a new loss function coined categorical margin maximisation loss (C‐marmax) is proposed, which gradually improves the performance against the most powerful attack. The proposed approach provides a more balanced performance across known and unknown attacks and achieves state‐of‐the‐art performance in known and unknown attack detection cases against rational attackers. Lastly, the few‐shot learning potential of the proposed approach as well as its ability to provide pixel‐level explainability is studied.https://doi.org/10.1049/bme2.12053game theorylearning (artificial intelligence)security of data |
| spellingShingle | Ali Khodabakhsh Zahid Akhtar Unknown presentation attack detection against rational attackers IET Biometrics game theory learning (artificial intelligence) security of data |
| title | Unknown presentation attack detection against rational attackers |
| title_full | Unknown presentation attack detection against rational attackers |
| title_fullStr | Unknown presentation attack detection against rational attackers |
| title_full_unstemmed | Unknown presentation attack detection against rational attackers |
| title_short | Unknown presentation attack detection against rational attackers |
| title_sort | unknown presentation attack detection against rational attackers |
| topic | game theory learning (artificial intelligence) security of data |
| url | https://doi.org/10.1049/bme2.12053 |
| work_keys_str_mv | AT alikhodabakhsh unknownpresentationattackdetectionagainstrationalattackers AT zahidakhtar unknownpresentationattackdetectionagainstrationalattackers |