2024
|
Rot, Peter; Terhorst, Philipp; Peer, Peter; Štruc, Vitomir ASPECD: Adaptable Soft-Biometric Privacy-Enhancement Using Centroid Decoding for Face Verification Proceedings Article In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp. 1-9, 2024. @inproceedings{Rot_FG2024,
title = {ASPECD: Adaptable Soft-Biometric Privacy-Enhancement Using Centroid Decoding for Face Verification},
author = {Peter Rot and Philipp Terhorst and Peter Peer and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/03/PeterRot_FG2024.pdf},
year = {2024},
date = {2024-05-28},
booktitle = {Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FG)},
pages = {1-9},
abstract = {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.},
keywords = {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},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
Rot, Peter; Križaj, Janez; Peer, Peter; Štruc, Vitomir Enhancing Gender Privacy with Photo-realistic Fusion of Disentangled Spatial Segments Proceedings Article In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5, 2024. @inproceedings{RotICASSP24,
title = {Enhancing Gender Privacy with Photo-realistic Fusion of Disentangled Spatial Segments},
author = {Peter Rot and Janez Križaj and Peter Peer and Vitomir Štruc},
year = {2024},
date = {2024-04-02},
booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {1-5},
keywords = {deep learning, face, privacy, privacy enhancement, privacy protection, privacy-enhancing techniques, soft biometric privacy},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2023
|
Rot, Peter; Grm, Klemen; Peer, Peter; Štruc, Vitomir PrivacyProber: Assessment and Detection of Soft–Biometric Privacy–Enhancing Techniques Journal Article In: IEEE Transactions on Dependable and Secure Computing, pp. 1-18, 2023, ISBN: 1545-5971. @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}
}
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. |
Meden, Blaž; Gonzalez-Hernandez, Manfred; Peer, Peter; Štruc, Vitomir Face deidentification with controllable privacy protection Journal Article In: Image and Vision Computing, vol. 134, no. 104678, pp. 1-19, 2023. @article{MedenDeID2023,
title = {Face deidentification with controllable privacy protection},
author = {Blaž Meden and Manfred Gonzalez-Hernandez and Peter Peer and Vitomir Štruc},
url = {https://reader.elsevier.com/reader/sd/pii/S0262885623000525?token=BC1E21411C50118E666720B002A89C9EB3DB4CFEEB5EB18D7BD7B0613085030A96621C8364583BFE7BAE025BE3646096&originRegion=eu-west-1&originCreation=20230516115322},
doi = {https://doi.org/10.1016/j.imavis.2023.104678},
year = {2023},
date = {2023-04-01},
journal = {Image and Vision Computing},
volume = {134},
number = {104678},
pages = {1-19},
abstract = {Privacy protection has become a crucial concern in today’s digital age. Particularly sensitive here are facial images, which typically not only reveal a person’s identity, but also other sensitive personal information. To address this problem, various face deidentification techniques have been presented in the literature. These techniques try to remove or obscure personal information from facial images while still preserving their usefulness for further analysis. While a considerable amount of work has been proposed on face deidentification, most state-of-theart solutions still suffer from various drawbacks, and (a) deidentify only a narrow facial area, leaving potentially important contextual information unprotected, (b) modify facial images to such degrees, that image naturalness and facial diversity is suffering in the deidentify images, (c) offer no flexibility in the level of privacy protection ensured, leading to suboptimal deployment in various applications, and (d) often offer an unsatisfactory tradeoff between the ability to obscure identity information, quality and naturalness of the deidentified images, and sufficient utility preservation. In this paper, we address these shortcomings with a novel controllable face deidentification technique that balances image quality, identity protection, and data utility for further analysis. The proposed approach utilizes a powerful generative model (StyleGAN2), multiple auxiliary classification models, and carefully designed constraints to guide the deidentification process. The approach is validated across four diverse datasets (CelebA-HQ, RaFD, XM2VTS, AffectNet) and in comparison to 7 state-of-the-art competitors. The results of the experiments demonstrate that the proposed solution leads to: (a) a considerable level of identity protection, (b) valuable preservation of data utility, (c) sufficient diversity among the deidentified faces, and (d) encouraging overall performance.},
keywords = {CNN, deep learning, deidentification, face recognition, GAN, GAN inversion, privacy, privacy protection, StyleGAN2},
pubstate = {published},
tppubtype = {article}
}
Privacy protection has become a crucial concern in today’s digital age. Particularly sensitive here are facial images, which typically not only reveal a person’s identity, but also other sensitive personal information. To address this problem, various face deidentification techniques have been presented in the literature. These techniques try to remove or obscure personal information from facial images while still preserving their usefulness for further analysis. While a considerable amount of work has been proposed on face deidentification, most state-of-theart solutions still suffer from various drawbacks, and (a) deidentify only a narrow facial area, leaving potentially important contextual information unprotected, (b) modify facial images to such degrees, that image naturalness and facial diversity is suffering in the deidentify images, (c) offer no flexibility in the level of privacy protection ensured, leading to suboptimal deployment in various applications, and (d) often offer an unsatisfactory tradeoff between the ability to obscure identity information, quality and naturalness of the deidentified images, and sufficient utility preservation. In this paper, we address these shortcomings with a novel controllable face deidentification technique that balances image quality, identity protection, and data utility for further analysis. The proposed approach utilizes a powerful generative model (StyleGAN2), multiple auxiliary classification models, and carefully designed constraints to guide the deidentification process. The approach is validated across four diverse datasets (CelebA-HQ, RaFD, XM2VTS, AffectNet) and in comparison to 7 state-of-the-art competitors. The results of the experiments demonstrate that the proposed solution leads to: (a) a considerable level of identity protection, (b) valuable preservation of data utility, (c) sufficient diversity among the deidentified faces, and (d) encouraging overall performance. |
2022
|
Osorio-Roig, Daile; Rathgeb, Christian; Drozdowski, Pawel; Terhörst, Philipp; Štruc, Vitomir; Busch, Christoph An Attack on Feature Level-based Facial Soft-biometric Privacy Enhancement Journal Article In: IEEE Transactions on Biometrics, Identity and Behavior (TBIOM), vol. 4, iss. 2, pp. 263-275, 2022. @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}
}
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. |
2021
|
Peter Rot Blaz Meden, Philipp Terhorst Privacy-Enhancing Face Biometrics: A Comprehensive Survey Journal Article In: IEEE Transactions on Information Forensics and Security, vol. 16, pp. 4147-4183, 2021. @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}
}
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. |
2020
|
Marco Huber Philipp Terhörst, Naser Damer Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies Proceedings Article In: Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG) 2020, pp. 1-5, IEEE, 2020, ISSN: 1617-5468. @inproceedings{Biosig_naser_2020,
title = {Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies},
author = {Philipp Terhörst, Marco Huber, Naser Damer, Peter Rot, Florian Kirchbuchner, Vitomir Struc, Arjan Kuijper},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2020/11/Biosig_privacy.pdf},
issn = {1617-5468},
year = {2020},
date = {2020-09-16},
booktitle = {Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG) 2020},
pages = {1-5},
publisher = {IEEE},
abstract = {Biometric data includes privacy-sensitive information, such as soft-biometrics. Soft-biometric privacy enhancing technologies aim at limiting the possibility of deducing such information. Previous works proposed several solutions to this problem using several different evaluation processes, metrics, and attack scenarios. The absence of a standardized evaluation protocol makes a meaningful comparison of these solutions difficult. In this work, we propose privacy evaluation protocols (PEPs) for privacy-enhancing technologies (PETs) dealing with soft-biometric privacy. Our framework evaluates PETs in the most critical scenario of an attacker that knows and adapts to the systems privacy-mechanism. Moreover, our PEPs differentiate between PET of learning-based or training-free nature. To ensure that our protocol meets the highest standards in both cases, it is based on Kerckhoffs‘s principle of cryptography.},
keywords = {face recognition, privacy, privacy protection, soft biometric privacy},
pubstate = {published},
tppubtype = {inproceedings}
}
Biometric data includes privacy-sensitive information, such as soft-biometrics. Soft-biometric privacy enhancing technologies aim at limiting the possibility of deducing such information. Previous works proposed several solutions to this problem using several different evaluation processes, metrics, and attack scenarios. The absence of a standardized evaluation protocol makes a meaningful comparison of these solutions difficult. In this work, we propose privacy evaluation protocols (PEPs) for privacy-enhancing technologies (PETs) dealing with soft-biometric privacy. Our framework evaluates PETs in the most critical scenario of an attacker that knows and adapts to the systems privacy-mechanism. Moreover, our PEPs differentiate between PET of learning-based or training-free nature. To ensure that our protocol meets the highest standards in both cases, it is based on Kerckhoffs‘s principle of cryptography. |
2018
|
Meden, Blaz; Peer, Peter; Struc, Vitomir Selective Face Deidentification with End-to-End Perceptual Loss Learning Proceedings Article In: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), pp. 1–7, IEEE 2018. @inproceedings{meden2018selective,
title = {Selective Face Deidentification with End-to-End Perceptual Loss Learning},
author = {Blaz Meden and Peter Peer and Vitomir Struc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/Selective_Face_Deidentification_with_End_to_End_Perceptual_Loss_Learning.pdf},
year = {2018},
date = {2018-06-01},
booktitle = {2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)},
pages = {1--7},
organization = {IEEE},
abstract = {Privacy is a highly debatable topic in the modern technological era. With the advent of massive video and image data (which in a lot of cases contains personal information on the recorded subjects), there is an imminent need for efficient privacy protection mechanisms. To this end, we develop in this work a novel Face Deidentification Network (FaDeNet) that is able to alter the input faces in such a way that automated recognition fail to recognize the subjects in the images, while this is still possible for human observers. FaDeNet is based an encoder-decoder architecture that is trained to auto-encode the input image, while (at the same time) minimizing the recognition performance of a secondary network that is used as an socalled identity critic in FaDeNet. We present experiments on the Radbound Faces Dataset and observe encouraging results.},
keywords = {deidentification, face, face deidentification, privacy protection},
pubstate = {published},
tppubtype = {inproceedings}
}
Privacy is a highly debatable topic in the modern technological era. With the advent of massive video and image data (which in a lot of cases contains personal information on the recorded subjects), there is an imminent need for efficient privacy protection mechanisms. To this end, we develop in this work a novel Face Deidentification Network (FaDeNet) that is able to alter the input faces in such a way that automated recognition fail to recognize the subjects in the images, while this is still possible for human observers. FaDeNet is based an encoder-decoder architecture that is trained to auto-encode the input image, while (at the same time) minimizing the recognition performance of a secondary network that is used as an socalled identity critic in FaDeNet. We present experiments on the Radbound Faces Dataset and observe encouraging results. |
Meden, Blaž; Emeršič, Žiga; Štruc, Vitomir; Peer, Peter k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification Journal Article In: Entropy, vol. 20, no. 1, pp. 60, 2018. @article{meden2018k,
title = {k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification},
author = {Blaž Meden and Žiga Emeršič and Vitomir Štruc and Peter Peer},
url = {https://www.mdpi.com/1099-4300/20/1/60/pdf},
year = {2018},
date = {2018-01-01},
journal = {Entropy},
volume = {20},
number = {1},
pages = {60},
publisher = {Multidisciplinary Digital Publishing Institute},
abstract = {Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data after deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent Generative Neural Networks (GNNs) with the well-known k-Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for deidentification by seamlessly combining features of identities used to train the GNN model. Furthermore, it allows us to control the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender) of the synthesized surrogate images. We demonstrate the feasibility of k-Same-Net in comprehensive experiments on the XM2VTS and CK+ datasets. We evaluate the efficacy of the proposed approach through reidentification experiments with recent recognition models and compare our results with competing deidentification techniques from the literature. We also present facial expression recognition experiments to demonstrate the utility-preservation capabilities of k-Same-Net. Our experimental results suggest that k-Same-Net is a viable option for facial deidentification that exhibits several desirable characteristics when compared to existing solutions in this area.},
keywords = {deidentification, face, k-same, k-same-net, privacy protection},
pubstate = {published},
tppubtype = {article}
}
Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data after deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent Generative Neural Networks (GNNs) with the well-known k-Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for deidentification by seamlessly combining features of identities used to train the GNN model. Furthermore, it allows us to control the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender) of the synthesized surrogate images. We demonstrate the feasibility of k-Same-Net in comprehensive experiments on the XM2VTS and CK+ datasets. We evaluate the efficacy of the proposed approach through reidentification experiments with recent recognition models and compare our results with competing deidentification techniques from the literature. We also present facial expression recognition experiments to demonstrate the utility-preservation capabilities of k-Same-Net. Our experimental results suggest that k-Same-Net is a viable option for facial deidentification that exhibits several desirable characteristics when compared to existing solutions in this area. |
2017
|
Meden, Blaž; Malli, Refik Can; Fabijan, Sebastjan; Ekenel, Hazim Kemal; Štruc, Vitomir; Peer, Peter Face deidentification with generative deep neural networks Journal Article In: IET Signal Processing, vol. 11, no. 9, pp. 1046–1054, 2017. @article{meden2017face,
title = {Face deidentification with generative deep neural networks},
author = {Blaž Meden and Refik Can Malli and Sebastjan Fabijan and Hazim Kemal Ekenel and Vitomir Štruc and Peter Peer},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/Face_Deidentification_with_Generative_Deep_Neural_Networks.pdf},
year = {2017},
date = {2017-01-01},
journal = {IET Signal Processing},
volume = {11},
number = {9},
pages = {1046--1054},
publisher = {IET},
abstract = {Face deidentification is an active topic amongst privacy and security researchers. Early deidentification methods relying on image blurring or pixelisation have been replaced in recent years with techniques based on formal anonymity models that provide privacy guaranties and retain certain characteristics of the data even after deidentification. The latter aspect is important, as it allows the deidentified data to be used in applications for which identity information is irrelevant. In this work, the authors present a novel face deidentification pipeline, which ensures anonymity by synthesising artificial surrogate faces using generative neural networks (GNNs). The generated faces are used to deidentify subjects in images or videos, while preserving non-identity-related aspects of the data and consequently enabling data utilisation. Since generative networks are highly adaptive and can utilise diverse parameters (pertaining to the appearance of the generated output in terms of facial expressions, gender, race etc.), they represent a natural choice for the problem of face deidentification. To demonstrate the feasibility of the authors’ approach, they perform experiments using automated recognition tools and human annotators. Their results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is effective.},
keywords = {biometrics, computer vision, deidentification, face, privacy protection},
pubstate = {published},
tppubtype = {article}
}
Face deidentification is an active topic amongst privacy and security researchers. Early deidentification methods relying on image blurring or pixelisation have been replaced in recent years with techniques based on formal anonymity models that provide privacy guaranties and retain certain characteristics of the data even after deidentification. The latter aspect is important, as it allows the deidentified data to be used in applications for which identity information is irrelevant. In this work, the authors present a novel face deidentification pipeline, which ensures anonymity by synthesising artificial surrogate faces using generative neural networks (GNNs). The generated faces are used to deidentify subjects in images or videos, while preserving non-identity-related aspects of the data and consequently enabling data utilisation. Since generative networks are highly adaptive and can utilise diverse parameters (pertaining to the appearance of the generated output in terms of facial expressions, gender, race etc.), they represent a natural choice for the problem of face deidentification. To demonstrate the feasibility of the authors’ approach, they perform experiments using automated recognition tools and human annotators. Their results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is effective. |
Meden, Blaz; Emersic, Ziga; Struc, Vitomir; Peer, Peter k-Same-Net: Neural-Network-Based Face Deidentification Proceedings Article In: 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI), pp. 1–7, IEEE 2017. @inproceedings{meden2017kappa,
title = {k-Same-Net: Neural-Network-Based Face Deidentification},
author = {Blaz Meden and Ziga Emersic and Vitomir Struc and Peter Peer},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/k-same-net.pdf},
year = {2017},
date = {2017-01-01},
booktitle = {2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI)},
pages = {1--7},
organization = {IEEE},
abstract = {An increasing amount of video and image data is being shared between government entities and other relevant stakeholders and requires careful handling of personal information. A popular approach for privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent generative neural networks (GNNs) with the well-known k-anonymity mechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for dedentification by seamlessly combining features of identities used to train the GNN mode. furthermore, it allows us to guide the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender) of the synthesized surrogate images. We demonstrate the feasibility of k-Same-Net in comparative experiments with competing techniques on the XM2VTS dataset and discuss the main characteristics of our approach.},
keywords = {deidentification, face, privacy protection},
pubstate = {published},
tppubtype = {inproceedings}
}
An increasing amount of video and image data is being shared between government entities and other relevant stakeholders and requires careful handling of personal information. A popular approach for privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent generative neural networks (GNNs) with the well-known k-anonymity mechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for dedentification by seamlessly combining features of identities used to train the GNN mode. furthermore, it allows us to guide the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender) of the synthesized surrogate images. We demonstrate the feasibility of k-Same-Net in comparative experiments with competing techniques on the XM2VTS dataset and discuss the main characteristics of our approach. |
2014
|
Marčetić, Darijan; Ribarić, Slobodan; Štruc, Vitomir; Pavešić, Nikola An experimental tattoo de-identification system for privacy protection in still images Proceedings Article In: 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1288–1293, Mipro IEEE, 2014. @inproceedings{marcetic2014experimental,
title = {An experimental tattoo de-identification system for privacy protection in still images},
author = {Darijan Marčetić and Slobodan Ribarić and Vitomir Štruc and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/anexperimentaltattoode-identificationsystemforprivacyprotectioninstillimages/},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)},
pages = {1288--1293},
publisher = {IEEE},
organization = {Mipro},
abstract = {An experimental tattoo de-identification system for privacy protection in still images is described in the paper. The system consists of the following modules: skin detection, region of interest detection, feature extraction, tattoo database, matching, tattoo detection, skin swapping, and quality evaluation. Two methods for tattoo localization are presented. The first is a simple ad-hoc method based only on skin colour. The second is based on skin colour, texture and SIFT features. The appearance of each tattoo area is de-identified in such a way that its skin colour and skin texture are similar to the surrounding skin area. Experimental results for still images in which tattoo location, distance, size, illumination, and motion blur have large variability are presented. The system is subjectively evaluated based on the results of tattoo localization, the level of privacy protection and the naturalness of the de-identified still images. The level of privacy protection is estimated based on the quality of the removal of the tattoo appearance and the concealment of its location.},
keywords = {computer vision, deidentification, MIPRO, privacy protection, tattoo deidentification},
pubstate = {published},
tppubtype = {inproceedings}
}
An experimental tattoo de-identification system for privacy protection in still images is described in the paper. The system consists of the following modules: skin detection, region of interest detection, feature extraction, tattoo database, matching, tattoo detection, skin swapping, and quality evaluation. Two methods for tattoo localization are presented. The first is a simple ad-hoc method based only on skin colour. The second is based on skin colour, texture and SIFT features. The appearance of each tattoo area is de-identified in such a way that its skin colour and skin texture are similar to the surrounding skin area. Experimental results for still images in which tattoo location, distance, size, illumination, and motion blur have large variability are presented. The system is subjectively evaluated based on the results of tattoo localization, the level of privacy protection and the naturalness of the de-identified still images. The level of privacy protection is estimated based on the quality of the removal of the tattoo appearance and the concealment of its location. |