Detecting morphed face attacks using residual noise from deep multi-scale context aggregation network

Sushma Venkatesh, Raghavendra Ramachandra, Kiran Raja, Luuk Spreeuwers, Raymond Veldhuis, Christoph Busch

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

Along with the deployment of the Face Recognition Systems (FRS), concerns were raised related to the vulnerability of those systems towards various attacks including morphed attacks. The morphed face attack involves two different face images in order to obtain via a morphing process a resulting attack image, which is sufficiently similar to both contributing data subjects. The obtained morphed image can successfully be verified against both subjects visually (by a human expert) and by a commercial FRS. The face morphing attack poses a severe security risk to the e-passport issuance process and to applications like border control, unless such attacks are detected and mitigated. In this work, we propose a new method to reliably detect a morphed face attack using a newly designed demising framework. To this end, we design and introduce a new deep Multi-scale Context Aggregation Network (MS-CAN) to obtain denoised images, which is subsequently used to determine if an image is morphed or not. Extensive experiments are carried out on three different morphed face image datasets. The Morphing Attack Detection (MAD) performance of the proposed method is also benchmarked against 14 different state-of-the-art techniques using the ISO-IEC 30107-3 evaluation metrics. Based on the obtained quantitative results, the proposed method has indicated the best performance on all three datasets and also on cross-dataset experiments.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages269-278
Number of pages10
ISBN (Electronic)978-1-7281-6553-0, 978-1-7281-6552-3
ISBN (Print)978-1-7281-6554-7
DOIs
Publication statusPublished - Mar 2020
EventIEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 - Westin Snowmass Resort, Snowmass Village, United States
Duration: 1 Mar 20205 Mar 2020
https://wacv20.wacv.net/

Publication series

NameProceedings - IEEE Winter Conference on Applications of Computer Vision (WACV)
PublisherIEEE
Volume2020
ISSN (Print)2472-6737
ISSN (Electronic)2642-9381

Conference

ConferenceIEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020
Abbreviated titleWACV 2020
CountryUnited States
CitySnowmass Village
Period1/03/205/03/20
Internet address

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  • Cite this

    Venkatesh, S., Ramachandra, R., Raja, K., Spreeuwers, L., Veldhuis, R., & Busch, C. (2020). Detecting morphed face attacks using residual noise from deep multi-scale context aggregation network. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 269-278). [9093488] (Proceedings - IEEE Winter Conference on Applications of Computer Vision (WACV); Vol. 2020). Piscataway, NJ: IEEE. https://doi.org/10.1109/WACV45572.2020.9093488