Publications

@article{PrivacProberRot,

title = {PrivacyProber: Assessment and Detection of Soft–Biometric Privacy–Enhancing Techniques},

author = {Peter Rot and Klemen Grm and Peter Peer and Vitomir Štruc},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10264192},

doi = {10.1109/TDSC.2023.3319500},

isbn = {1545-5971},

year  = {2023},

date = {2023-09-23},

journal = {IEEE Transactions on Dependable and Secure Computing},

pages = {1-18},

abstract = {Soft–biometric privacy–enhancing techniques represent machine learning methods that aim to: (i) mitigate privacy concerns associated with face recognition technology by suppressing selected soft–biometric attributes in facial images (e.g., gender, age, ethnicity) and (ii) make unsolicited extraction of sensitive personal information infeasible. Because such techniques are increasingly used in real–world applications, it is imperative to understand to what extent the privacy enhancement can be inverted and how much attribute information can be recovered from privacy–enhanced images. While these aspects are critical, they have not been investigated in the literature so far. In this paper, we, therefore, study the robustness of several state–of–the–art soft–biometric privacy–enhancing techniques to attribute recovery attempts. We propose PrivacyProber, a high–level framework for restoring soft–biometric information from privacy–enhanced facial images, and apply it for attribute recovery in comprehensive experiments on three public face datasets, i.e., LFW, MUCT and Adience. Our experiments show that the proposed framework is able to restore a considerable amount of suppressed information, regardless of the privacy–enhancing technique used (e.g., adversarial perturbations, conditional synthesis, etc.), but also that there are significant differences between the considered privacy models. These results point to the need for novel mechanisms that can improve the robustness of existing privacy–enhancing techniques and secure them against potential adversaries trying to restore suppressed information. Additionally, we demonstrate that PrivacyProber can also be used to detect privacy–enhancement in facial images (under black–box assumptions) with high accuracy. Specifically, we show that a detection procedure can be developed around the proposed framework that is learning free and, therefore, generalizes well across different data characteristics and privacy–enhancing techniques.},

keywords = {biometrics, face, privacy, privacy enhancement, privacy protection, privacy-enhancing techniques, soft biometric privacy},

pubstate = {published},

tppubtype = {article}

}

@article{TBIOM_2022,

title = {An Attack on Feature Level-based Facial Soft-biometric Privacy Enhancement},

author = {Daile Osorio-Roig and Christian Rathgeb and Pawel Drozdowski and Philipp Terhörst and Vitomir Štruc and Christoph Busch},

url = {https://arxiv.org/pdf/2111.12405.pdf},

year  = {2022},

date = {2022-05-02},

urldate = {2022-05-02},

journal = {IEEE Transactions on Biometrics, Identity and Behavior (TBIOM)},

volume = {4},

issue = {2},

pages = {263-275},

abstract = {In the recent past, different researchers have proposed novel privacy-enhancing face recognition systems designed to conceal soft-biometric information at feature level. These works have reported impressive results, but usually do not consider specific attacks in their analysis of privacy protection. In most cases, the privacy protection capabilities of these schemes are tested through simple machine learning-based classifiers and visualisations of dimensionality reduction tools. In this work, we introduce an attack on feature level-based facial soft–biometric privacy-enhancement techniques. The attack is based on two observations: (1) to achieve high recognition accuracy, certain similarities between facial representations have to be retained in their privacy-enhanced versions; (2) highly similar facial representations usually originate from face images with similar soft-biometric attributes. Based on these observations, the proposed attack compares a privacy-enhanced face representation against a set of privacy-enhanced face representations with known soft-biometric attributes. Subsequently, the best obtained similarity scores are analysed to infer the unknown soft-biometric attributes of the attacked privacy-enhanced face representation. That is, the attack only requires a relatively small database of arbitrary face images and the privacy-enhancing face recognition algorithm as a black-box. In the experiments, the attack is applied to two representative approaches which have previously been reported to reliably conceal the gender in privacy-enhanced face representations. It is shown that the presented attack is able to circumvent the privacy enhancement to a considerable degree and is able to correctly classify gender with an accuracy of up to approximately 90% for both of the analysed privacy-enhancing face recognition systems. Future works on privacy-enhancing face recognition are encouraged to include the proposed attack in evaluations on privacy protection.},

keywords = {attack, face recognition, privacy, privacy enhancement, privacy protection, privacy-enhancing techniques, soft biometric privacy},

pubstate = {published},

tppubtype = {article}

}

@article{TIFS_PrivacySurveyb,

title = {Privacy-Enhancing Face Biometrics: A Comprehensive Survey},

author = {Blaz Meden, Peter Rot, Philipp Terhorst, Naser Damer, Arjan Kuijper, Walter J. Scheirer, Arun Ross, Peter Peer, Vitomir Struc},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9481149

https://lmi.fe.uni-lj.si/en/visual_privacy_of_faces__a_survey_preprint-compressed/},

doi = {10.1109/TIFS.2021.3096024},

year  = {2021},

date = {2021-07-12},

journal = {IEEE Transactions on Information Forensics and Security},

volume = {16},

pages = {4147-4183},

abstract = {Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios.  These efforts have resulted in a multitude of privacy--enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy--related research in the area of biometrics and review existing work on textit{Biometric Privacy--Enhancing Techniques} (B--PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible,  several key topics are covered as well, including evaluation strategies used with B--PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future. },

keywords = {biometrics, deidentification, face analysis, face deidentification, face recognition, face verification, FaceGEN, privacy, privacy protection, privacy-enhancing techniques, soft biometric privacy},

pubstate = {published},

tppubtype = {article}

}

@article{PEMIU_Access2020,

title = {PE-MIU: a training-free privacy-enhancing face recognition approach based on minimum information units},

author = {Philipp Terhorst and Kevin Riehl and Naser Damer and Peter Rot and Blaz Bortolato and Florian Kirchbuchner and Vitomir Struc and Arjan Kuijper},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9094207},

year  = {2020},

date = {2020-06-02},

journal = {IEEE Access},

volume = {2020},

abstract = {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},

keywords = {biometrics, face recognition, minimal information units, privacy, soft biometric privacy, soft biometrics},

pubstate = {published},

tppubtype = {article}

}