Super-Resolution through StyleGAN Regularized Latent Search: A Realism-Fidelity Trade-off

This paper addresses the problem of super-resolution: constructing a highly resolved (HR) image from a low resolved (LR) one. Recent unsupervised approaches search the latent space of a StyleGAN pre-trained on HR images, for the image that best downscales to the input LR image. However, they tend to produce out-of-domain images and fail to accurately reconstruct HR images that are far from the original domain. Our contribution is twofold. Firstly, we introduce a new regularizer to constrain the search in the latent space, ensuring that the inverted code lies in the original image manifold. Secondly, we further enhanced the reconstruction through expanding the image prior around the optimal latent code. Our results show that the proposed approach recovers realistic high-quality images for large magnification factors. Furthermore, for low magnification factors, it can still reconstruct details that the generator could not have produced otherwise. Altogether, our approach achieves a good trade-off between fidelity and realism for the super-resolution task

Aggregation and embedding for group membership verification

accepted at ICASSP 2019International audienceThis paper proposes a group membership verification protocol preventing the curious but honest server from reconstructing the enrolled signatures and inferring the identity of querying clients. The protocol quantizes the signatures into discrete embeddings, making reconstruction difficult. It also aggregates multiple embeddings into representative values, impeding identification. Theoretical and experimental results show the trade-off between the security and error rates

Identification sécurisée pour Internet des objets

This thesis addresses the problem of authentication of low-power devices in the Internet of Things by introducing new functionalities: group membership verification and identification. The procedure verifies if a given IoT device is a member of a group without revealing the identity of that member. Similarly, group membership identification states which group the device belongs to without knowing the identity. We propose a protocol through the joint use of two mechanisms: quantizing templates into discrete embeddings, making reconstruction difficult, and aggregating several templates into one group representation, impeding identification. First, we consider two independent procedures, one for embedding, the other for aggregating. Then, we replace those deterministic functions with functions whose parameters are learned through optimization. Finally, rather than considering group assignments that are predetermined, group assignments are also learned together with representations of the groups. Our experiments show that learning yields an excellent trade-off between security/privacy and verification/identification performances. We also investigate the impact of the sparsity level of the features representing group members on both security and verification performances. It shows it is possible to trade compactness and sparsity for better security or better verification performance.Cette thèse aborde le problème de l’authentification des dispositifs à faible puissance dans l’Internet des objets en introduisant de nouvelles fonctionnalités : la vérification de l’appartenance à un groupe et l’identification. La procédure vérifie si un dispositif IoT donné est membre d’un groupe sans révéler l’identité de ce membre. De même, l’identification de l’appartenance à un groupe indique à quel groupe le dispositif appartient sans connaître son identité. Nous proposons un protocole par l’utilisation conjointe de deux mécanismes : la quantification des motifs dans des plongement discrets, rendant la reconstruction difficile, et l’agrégation de plusieurs motifs dans une représentation de groupe, entravant l’identification. Tout d’abord, nous considérons deux procédures indépendantes, l’une pour l’plongement, l’autre pour l’agrégation. Ensuite, nous remplaçons ces fonctions déterministes par des fonctions dont les paramètres sont appris par optimisation. Enfin, plutôt que de considérer des affectations de groupes prédéterminées, les affectations de groupes sont également apprises avec les représentations des groupes. Nos expériences montrent que l’apprentissage permet un excellent compromis entre les performances de sécurité/confidentialité et de vérification/identification. Nous étudions également l’impact du niveau de sparsité des fonctionnalités représentant les membres du groupe sur les performances de sécurité et de vérification. Nous montrons qu’il est possible d’échanger la compacité et la sparsité pour une meilleure sécurité ou de meilleures performances de vérification

Identification sécurisée pour Internet des objets

This thesis addresses the problem of authentication of low-power devices in the Internet of Things by introducing new functionalities: group membership verification and identification. The procedure verifies if a given IoT device is a member of a group without revealing the identity of that member. Similarly, group membership identification states which group the device belongs to without knowing the identity. We propose a protocol through the joint use of two mechanisms: quantizing templates into discrete embeddings, making reconstruction difficult, and aggregating several templates into one group representation, impeding identification. First, we consider two independent procedures, one for embedding, the other for aggregating. Then, we replace those deterministic functions with functions whose parameters are learned through optimization. Finally, rather than considering group assignments that are predetermined, group assignments are also learned together with representations of the groups. Our experiments show that learning yields an excellent trade-off between security/privacy and verification/identification performances. We also investigate the impact of the sparsity level of the features representing group members on both security and verification performances. It shows it is possible to trade compactness and sparsity for better security or better verification performance.Cette thèse aborde le problème de l’authentification des dispositifs à faible puissance dans l’Internet des objets en introduisant de nouvelles fonctionnalités : la vérification de l’appartenance à un groupe et l’identification. La procédure vérifie si un dispositif IoT donné est membre d’un groupe sans révéler l’identité de ce membre. De même, l’identification de l’appartenance à un groupe indique à quel groupe le dispositif appartient sans connaître son identité. Nous proposons un protocole par l’utilisation conjointe de deux mécanismes : la quantification des motifs dans des plongement discrets, rendant la reconstruction difficile, et l’agrégation de plusieurs motifs dans une représentation de groupe, entravant l’identification. Tout d’abord, nous considérons deux procédures indépendantes, l’une pour l’plongement, l’autre pour l’agrégation. Ensuite, nous remplaçons ces fonctions déterministes par des fonctions dont les paramètres sont appris par optimisation. Enfin, plutôt que de considérer des affectations de groupes prédéterminées, les affectations de groupes sont également apprises avec les représentations des groupes. Nos expériences montrent que l’apprentissage permet un excellent compromis entre les performances de sécurité/confidentialité et de vérification/identification. Nous étudions également l’impact du niveau de sparsité des fonctionnalités représentant les membres du groupe sur les performances de sécurité et de vérification. Nous montrons qu’il est possible d’échanger la compacité et la sparsité pour une meilleure sécurité ou de meilleures performances de vérification

Group Membership Verification with Privacy: Sparse or Dense?

International audienceGroup membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Recent contributions provide privacy for group membership protocols through the joint use of two mechanisms: quantizing templates into discrete embeddings, and aggregating several templates into one group representation. However, this scheme has one drawback: the data structure representing the group has a limited size and cannot recognize noisy query when many templates are aggregated. Moreover, the sparsity of the embeddings seemingly plays a crucial role on the performance verification. This paper proposes a mathematical model for group membership verification allowing to reveal the impact of sparsity on both security, compactness, and verification performances. This models bridges the gap towards a Bloom filter robust to noisy queries. It shows that a dense solution is more competitive unless the queries are almost noiseless

Joint Learning of Assignment and Representation for Biometric Group Membership

International audienceThis paper proposes a framework for group membership protocols preventing the curious but honest server from reconstructing the enrolled biometric signatures and inferring the identity of querying clients. This framework learns the embedding parameters, group representations, and assignments simultaneously. Experiments show the trade-off between security/privacy and verification/identification performances

Privacy Preserving Group Membership Verification and Identification

International audienceWhen convoking privacy, group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Similarly , group membership identification states which group the individual belongs to, without knowing his/her identity. A recent contribution provides privacy and security for group membership protocols through the joint use of two mechanisms: quantizing biometric templates into discrete embeddings, and aggregating several templates into one group representation. This paper significantly improves that contribution because it jointly learns how to embed and aggregate instead of imposing fixed and hard coded rules. This is demonstrated by exposing the mathematical underpinnings of the learning stage before showing the improvements through an extensive series of experiments targeting face recognition. Overall, experiments show that learning yields an excellent trade-off between security / privacy and the verification / identification performances