Venkatesh, Sushma ; Zhang, Haoyu ; Ramachandra, Raghavendra ; Raja, Kiran ; Damer, Naser ; Busch, Christoph (2020)
Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? - Vulnerability and Detection.
Porto, Portugal (29.04.2020-30.04.2020)
doi: 10.1109/IWBF49977.2020.9107970
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The primary objective of face morphing is to com-bine face images of different data subjects (e.g. an malicious actor and an accomplice) to generate a face image that can be equally verified for both contributing data subjects. In this paper, we propose a new framework for generating face morphs using a newer Generative Adversarial Network (GAN) - StyleGAN. In contrast to earlier works, we generate realistic morphs of both high-quality and high resolution of 1024 × 1024 pixels. With the newly created morphing dataset of 2500 morphed face images, we pose a critical question in this work. (i) Can GAN generated morphs threaten Face Recognition Systems (FRS) equally as Landmark based morphs? Seeking an answer, we benchmark the vulnerability of a Commercial-Off-The-Shelf FRS (COTS) and a deep learning-based FRS (ArcFace). This work also benchmarks the detection approaches for both GAN generated morphs against the landmark based morphs using established Morphing Attack Detection (MAD) schemes.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Venkatesh, Sushma ; Zhang, Haoyu ; Ramachandra, Raghavendra ; Raja, Kiran ; Damer, Naser ; Busch, Christoph |
| Art des Eintrags: | Bibliographie |
| Titel: | Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? - Vulnerability and Detection |
| Sprache: | Englisch |
| Publikationsjahr: | 2020 |
| Ort: | Los Alamitos, Calif. |
| Buchtitel: | 2020 8th International Workshop on Biometrics and Forensics (IWBF) |
| Veranstaltungsort: | Porto, Portugal |
| Veranstaltungsdatum: | 29.04.2020-30.04.2020 |
| DOI: | 10.1109/IWBF49977.2020.9107970 |
| URL / URN: | https://doi.org/10.1109/IWBF49977.2020.9107970 |
| Kurzbeschreibung (Abstract): | The primary objective of face morphing is to com-bine face images of different data subjects (e.g. an malicious actor and an accomplice) to generate a face image that can be equally verified for both contributing data subjects. In this paper, we propose a new framework for generating face morphs using a newer Generative Adversarial Network (GAN) - StyleGAN. In contrast to earlier works, we generate realistic morphs of both high-quality and high resolution of 1024 × 1024 pixels. With the newly created morphing dataset of 2500 morphed face images, we pose a critical question in this work. (i) Can GAN generated morphs threaten Face Recognition Systems (FRS) equally as Landmark based morphs? Seeking an answer, we benchmark the vulnerability of a Commercial-Off-The-Shelf FRS (COTS) and a deep learning-based FRS (ArcFace). This work also benchmarks the detection approaches for both GAN generated morphs against the landmark based morphs using established Morphing Attack Detection (MAD) schemes. |
| Freie Schlagworte: | Biometrics, Face recognition, Spoofing attacks |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 08 Jun 2020 10:20 |
| Letzte Änderung: | 08 Jun 2020 10:20 |
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