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 [BibTeX] [Marc21]
Vulnerability of State-of-the-Art Face Recognition Models to Template Inversion Attack
Type of publication: Journal paper
Citation: OtroshiShahreza_IEEE-TIFS_2024
Publication status: Published
Journal: IEEE Transactions on Information Forensics and Security
Volume: 19
Year: 2024
Pages: 4585-4600
ISSN: 1556-6013 1556-6021
URL: https://ieeexplore.ieee.org/do...
DOI: 10.1109/TIFS.2024.3381820
Abstract: Face recognition systems use the templates (extracted from users’ face images) stored in the system’s database for recognition. In a template inversion attack, the adversary gains access to the stored templates and tries to enter the system using images reconstructed from those templates. In this paper, we propose a framework to evaluate the vulnerability of face recognition systems to template inversion attacks. We build our framework upon a real-world scenario and measure the vulnerability of the system in terms of the adversary’s success attack rate in entering the system using the reconstructed face images. We propose a face reconstruction network based on a new block called “enhanced deconvolution using cascaded convolution and skip connections” (shortly, DSCasConv), and train it with a multi-term loss function. We use our framework to evaluate the vulnerability of state-of-the-art face recognition models, with different network structures and loss functions (in total 31 models), on the MOBIO, LFW, and AgeDB face datasets. Our experiments show that the reconstructed face images can be used to enter the system, which threatens the system’s security. Additionally, the reconstructed face images may reveal important information about each user’s identity, such as race, gender, and age, and hence jeopardize the users’ privacy.
Keywords: Biometrics, embedding, Face Recognition, face reconstruction, template inversion, vulnerability evaluation
Authors Otroshi Shahreza, Hatef
Krivokuca, Vedrana
Marcel, Sébastien
Added by: [UNK]
Total mark: 0
  • OtroshiShahreza_IEEE-TIFS_2024.pdf