2024 |
Rot, Peter; Terhorst, Philipp; Peer, Peter; Štruc, Vitomir ASPECD: Adaptable Soft-Biometric Privacy-Enhancement Using Centroid Decoding for Face Verification Proceedings Article V: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FG), str. 1-9, 2024. Povzetek | Povezava | BibTeX | Oznake: deepfake, deepfakes, face, face analysis, face deidentification, face image processing, face images, face synthesis, face verification, privacy, privacy enhancement, privacy protection, privacy-enhancing techniques, soft biometric privacy, soft biometrics @inproceedings{Rot_FG2024, State-of-the-art face recognition models commonly extract information-rich biometric templates from the input images that are then used for comparison purposes and identity inference. While these templates encode identity information in a highly discriminative manner, they typically also capture other potentially sensitive facial attributes, such as age, gender or ethnicity. To address this issue, Soft-Biometric Privacy-Enhancing Techniques (SB-PETs) were proposed in the literature that aim to suppress such attribute information, and, in turn, alleviate the privacy risks associated with the extracted biometric templates. While various SB-PETs were presented so far, existing approaches do not provide dedicated mechanisms to determine which soft-biometrics to exclude and which to retain. In this paper, we address this gap and introduce ASPECD, a modular framework designed to selectively suppress binary and categorical soft-biometrics based on users' privacy preferences. ASPECD consists of multiple sequentially connected components, each dedicated for privacy-enhancement of an individual soft-biometric attribute. The proposed framework suppresses attribute information using a Moment-based Disentanglement process coupled with a centroid decoding procedure, ensuring that the privacy-enhanced templates are directly comparable to the templates in the original embedding space, regardless of the soft-biometric modality being suppressed. To validate the performance of ASPECD, we conduct experiments on a large-scale face dataset and with five state-of-the-art face recognition models, demonstrating the effectiveness of the proposed approach in suppressing single and multiple soft-biometric attributes. Our approach achieves a competitive privacy-utility trade-off compared to the state-of-the-art methods in scenarios that involve enhancing privacy w.r.t. gender and ethnicity attributes. Source code will be made publicly available. |
2020 |
Terhorst, Philipp; Riehl, Kevin; Damer, Naser; Rot, Peter; Bortolato, Blaz; Kirchbuchner, Florian; Struc, Vitomir; Kuijper, Arjan PE-MIU: a training-free privacy-enhancing face recognition approach based on minimum information units Članek v strokovni reviji V: IEEE Access, vol. 2020, 2020. Povzetek | Povezava | BibTeX | Oznake: biometrics, face recognition, minimal information units, privacy, soft biometric privacy, soft biometrics @article{PEMIU_Access2020, Research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric data. Utilizing biometric templates only, information about a persons gender, age, ethnicity, sexual orientation, and health state can be deduced. For many applications, these templates are expected to be used for recognition purposes only. Thus, extracting this information raises major privacy issues. Previous work proposed two kinds of learning-based solutions for this problem. The first ones provide strong privacy-enhancements, but limited to pre-defined attributes. The second ones achieve more comprehensive but weaker privacy-improvements. In this work, we propose a Privacy-Enhancing face recognition approach based on Minimum Information Units (PE-MIU). PE-MIU, as we demonstrate in this work, is a privacy-enhancement approach for face recognition templates that achieves strong privacy-improvements and is not limited to pre-defined attributes. We exploit the structural differences between face recognition and facial attribute estimation by creating templates in a mixed representation of minimal information units. These representations contain pattern of privacy-sensitive attributes in a highly randomized form. Therefore, the estimation of these attributes becomes hard for function creep attacks. During verification, these units of a probe template are assigned to the units of a reference template by solving an optimal best-matching problem. This allows our approach to maintain a high recognition ability. The experiments are conducted on three publicly available datasets and with five state-of-the-art approaches. Moreover, we conduct the experiments simulating an attacker that knows and adapts to the systems privacy mechanism. The experiments demonstrate that PE-MIU is able to suppress privacy-sensitive information to a significantly higher degree than previous work in all investigated scenarios. At the same time, our solution is able to achieve a verification performance close to that of the unmodified recognition system. Unlike previous works, our approach offers a strong and comprehensive privacy-enhancement without the need of training |
Objave
2024 |
ASPECD: Adaptable Soft-Biometric Privacy-Enhancement Using Centroid Decoding for Face Verification Proceedings Article V: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FG), str. 1-9, 2024. |
2020 |
PE-MIU: a training-free privacy-enhancing face recognition approach based on minimum information units Članek v strokovni reviji V: IEEE Access, vol. 2020, 2020. |