OTB-morph: One-time Biometrics via Morphing

Ghafourian, Mahdi; Fiérrez, Julián; Vera-Rodríguez, Rubén; Morales, Aythami; Serna, Ignacio

doi:10.1007/s11633-023-1432-x

Mach. Intell. Res.

2023

DOI: 10.1007/s11633-023-1432-x

|View full text |Cite

OTB-morph: One-time Biometrics via Morphing

Mahdi Ghafourian

Julián Fiérrez

Rubén Vera-Rodríguez

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Optimizing Key-Selection for Face-Based One-Time Biometrics via Morphing

Osorio-Roig,

Ghafourian,

Rathgeb

et al. 2023

2023 IEEE International Workshop on Information Forensics and Security (WIFS)

View full text Add to dashboard Cite

Nowadays, facial recognition systems are still vulnerable to adversarial attacks. These attacks vary from simple perturbations of the input image to modifying the parameters of the recognition model to impersonate an authorised subject. So-called privacy-enhancing facial recognition systems have been mostly developed to provide protection of stored biometric reference data, i.e. templates. In the literature, privacyenhancing facial recognition approaches have focused solely on conventional security threats at the template level, ignoring the growing concern related to adversarial attacks. Up to now, few works have provided mechanisms to protect face recognition against adversarial attacks while maintaining high security at the template level. In this paper, we propose different key selection strategies to improve the security of a competitive cancelable scheme operating at the signal level. Experimental results show that certain strategies based on signal-level key selection can lead to complete blocking of the adversarial attack based on an iterative optimization for the most secure threshold, while for the most practical threshold, the attack success chance can be decreased to approximately 5.0%.

show abstract

Optimizing Key-Selection for Face-Based One-Time Biometrics via Morphing

Osorio-Roig,

Ghafourian,

Rathgeb

et al. 2023

2023 IEEE International Workshop on Information Forensics and Security (WIFS)

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

OTB-morph: One-time Biometrics via Morphing

Cited by 1 publication

References 50 publications

Optimizing Key-Selection for Face-Based One-Time Biometrics via Morphing

Optimizing Key-Selection for Face-Based One-Time Biometrics via Morphing

Contact Info

Product

Resources

About