A Novel Cancelable FaceHashing Technique Based on Non-Invertible Transformation With Encryption and Decryption Template

Sardar, Alamgir; Umer, Saiyed; Pero, Chiara; Nappi, Michele

doi:10.1109/access.2020.2999656

Cited by 34 publications

(28 citation statements)

References 64 publications

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“…According to Drozdowski et al [11], only a few works have combined WR strategies with BTP schemes to build large-scale identification systems that ensure privacy protection. In the context of face biometrics, those studies have mainly employed cancelable biometrics [19]- [21]. However, most of those systems still report a degradation w.r.t.…”

Section: O(1)mentioning

confidence: 99%

“…In contrast, the approach in [19] achieved competitive results with respect to its baseline (i.e., unprotected system) by employing different fusion strategies. Inspired by this idea, Sardar et al [21] introduced a novel hashing technique, namely FaceHashing. Within this method, privacy protection is achieved by modifying the BioHashing technique [39], [40].…”

Section: Related Workmentioning

confidence: 99%

“…In addition, not all methods have properly shown, that they can reach unlinkability for cross-comparison attacks [15]. Further, WR strategies employed by these systems (e.g., [19]- [21]) are limited to feature transformation or exhaustive pre-selection approaches (e.g., [25]).…”

Section: Related Workmentioning

confidence: 99%

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Stable Hash Generation for Efficient Privacy-Preserving Face Identification

Osorio-Roig

Rathgeb

Drozdowski

et al. 2022

IEEE Trans. Biom. Behav. Identity Sci.

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The development of large-scale facial identification systems that provide privacy protection of the enrolled subjects represents an open challenge. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these schemes appear to be unsuitable for indexing (workload reduction) in biometric identification systems. More precisely, they have been utilised in identification systems performing exhaustive searches, thereby leading to degradations of the computational efficiency. In this work, we propose a privacypreserving face identification system which utilises a Product Quantisation-based hash look-up table for indexing and retrieval of protected face templates. These face templates are protected through fully homomorphic encryption schemes, thereby guaranteeing high privacy protection of the enrolled subjects. For the best configuration, the experimental evaluation carried out over closed-set and open-set settings shows the feasibility of the proposed technique for the use in large-scale facial identification systems: a workload reduction down to 0.1% of a baseline approach performing an exhaustive search is achieved together with a low pre-selection error rate of less than 1%. In terms of biometric performance, a False Negative Identification Rate (FNIR) in range of 0.0% -0.2% is obtained for practical False Positive Identification Rate (FPIR) values on the FEI and FERET face databases. In addition, our proposal shows competitive performance on unconstrained databases, e.g., the LFW face database. To the best of the authors' knowledge, this is the first work presenting a competitive privacy-preserving workload reduction scheme which performs template comparisons in the encrypted domain.

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Section: O(1)mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Stable Hash Generation for Efficient Privacy-Preserving Face Identification

Osorio-Roig

Rathgeb

Drozdowski

et al. 2022

IEEE Trans. Biom. Behav. Identity Sci.

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“…Sardar et al, proposed a hybrid solution by combining the non-invertibility with a biocode encryption. This helped in enhancing security levels along with better accuracy [39]. Talreja et al…”

Section: Literature Survey At a Glancementioning

confidence: 99%

PlexNet: An Ensemble of Deep Neural Networks for Biometric Template Protection

Singh¹,

Srivastva²,

Narain³

2021

IJACSA

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The security of biometric systems, especially protecting the templates stored in the gallery database, is a primary concern for researchers. This paper presents a novel framework using an ensemble of deep neural networks to protect biometric features stored as a template. The proposed ensemble chooses two state-of-the-art CNN architectures i.e., ResNet and DenseNet as base models for training. While training, the pre-trained weights enable the learning algorithm to converge faster. The weights obtained through the base model is further used to train other compatible models, generating a fine-tuned model. Thus, four fine-tuned models are prepared, and their learning are fused to form an ensemble named as PlexNet. To analyze biometric templates' security, the rigorous learning of ensemble is collected using a smart box i.e., application programming interface (API). The API is robust and correctly identifies the query image without referring to a template database. Thus, the proposed framework excludes the templates from database and performed predictions based on learning that is irrevocable.

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“…en, to provide greater security, they adapt the RSA algorithm [13]. Asgari-Chenaghlu et al use YoloV3 to automatically detect objects or the human body of the ROI delimiter and utilize a cryptosystem based on the chaotic sine map and logistic map systems.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Cryptosystem for Selective Encryption of Faces in Photographs

Santiago

Siordia

Bonilla

et al. 2020

Security and Communication Networks

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In this article, a safe communication system is proposed that implements one or more portable devices denominated SBC (single-board computers), with which photographs are taken and that later utilizes the OpenCV Library for the detection and identification of the faces that appear in them. Subsequently, it consults the information in a stored database, whether locally in SBC or in a remote server, to verify that the faces should be coded, and it encrypts these, implementing a new cryptosystem that executes mathematical models to generate chaotic orbits, one of which is used for application on two occasions the technique of diffusion with the purpose of carrying out a small change in one of the pixels of the image, generating very different cryptograms. In addition, in order to make a safer system, it implements other chaotic orbits during the technique of confusion. With the purpose of verifying the robustness of the encryption algorithm, a statistical analysis is performed employing histograms, horizontal, vertical, and diagonal correlation diagrams, entropy, number of pixel change rate (NPCR), unified average change intensity (UACI), sensitivity of the key, encryption quality analysis, and the avalanche effect. The cryptosystem is very robust in that it generates highly disordered cryptograms, supports differential attacks, and in addition is highly sensitive to changes in the pixels as well as in the encrypted keys.

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A Novel Cancelable FaceHashing Technique Based on Non-Invertible Transformation With Encryption and Decryption Template

Cited by 34 publications

References 64 publications

Stable Hash Generation for Efficient Privacy-Preserving Face Identification

Stable Hash Generation for Efficient Privacy-Preserving Face Identification

PlexNet: An Ensemble of Deep Neural Networks for Biometric Template Protection

Chaotic Cryptosystem for Selective Encryption of Faces in Photographs

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