A generic framework for deep incremental cancelable template generation

Singh, Avantika; Vashist, Chirag; Gaurav, Pratyush; Nigam, Aditya

doi:10.1016/j.neucom.2021.09.055

Cited by 11 publications

(5 citation statements)

References 50 publications

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“…Likewise, despite overcoming inversion, brute-force, and ARM using adaptive, Mohamed et al [105] is able to use the same approach to wade substitution and presentation attacks on multimodal features. Similarly, Walia et al [112] utilized the same deep feature unification approach for evading brute-force and ARM attacks to overcome dictionary and substitution attacks, while Singh et al [114] also mitigated attacks such as zero effort, stolen biometrics, and stolen key attacks from iris and knuckle features using the phase-wise incremental learning approach.…”

Section: Discussion On Other Attack Scenariosmentioning

confidence: 99%

“…Finally, a segment-clustering loss, a pairwise Hamming loss, and two classification losses are utilized for training. On the other hand, Singh et al [114] proposed a scheme that iteratively generates deep cancelable templates using deep networks. The architecture comprises of a lightweight CNN with minimal shot enrollment.…”

Section: ) Mechanisms Against Inversion Using Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

Biometric Template Attacks and Recent Protection Mechanisms: A Survey

Abdullahi,

Sun,

Wang

et al. 2023

Preprint

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Section: Discussion On Other Attack Scenariosmentioning

confidence: 99%

Section: ) Mechanisms Against Inversion Using Deep Learningmentioning

confidence: 99%

Biometric Template Attacks and Recent Protection Mechanisms: A Survey

Abdullahi,

Sun,

Wang

et al. 2023

Preprint

View full text Add to dashboard Cite

“… Hybrid‐based : This type of transformation function uses a combination of two or more of the above schemes (e.g. random projection + hashing [34]) to generate cancellable biometric templates.…”

Section: Different Types Of Cancellable Transformation Functionsmentioning

confidence: 99%

“…Both random projection and random cross-folding are employed to achieve irreversibility. To address the security and privacy issues of biometric templates generated through deep networks, Singh et al [34] devised a lightweight CNN-based cancellable biometric authentication method. In this method, biometric templates are cast onto a random subspace with an n-bit unique code retrieved by a deep biometric feature extraction network that is robustly trained.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Feature extraction and learning approaches for cancellable biometrics: A survey

Yang,

Wang,

et al. 2024

CAAI Trans on Intel Tech

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Biometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique for protecting biometric data. Regarding recognition accuracy, feature representation plays a significant role in the performance and reliability of cancellable biometric systems. How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community, especially from researchers of cancellable biometrics. Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance, while the privacy of biometric data is protected. This survey informs the progress, trend and challenges of feature extraction and learning for cancellable biometrics, thus shedding light on the latest developments and future research of this area.

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“…Cancelable biometrics schemes are being significantly used nowadays. In general, a cancelable biometric scheme depends on extracting various facial, iris, and fingerprint features in order to authenticate individuals in a real-time manner [1]. These extracted features of the obtained biometrics structure the cancelable biometric templates.…”

Section: Introductionmentioning

confidence: 99%

Proposed Biometric Security System Based on Deep Learning and Chaos燗lgorithms

Almomani¹,

El‐Shafai²,

Alkhayer³

et al. 2023

Computers, Materials &Amp; Continua

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Nowadays, there is tremendous growth in biometric authentication and cybersecurity applications. Thus, the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private sectors. Therefore, designing and implementing robust security algorithms for users' biometrics is still a hot research area to be investigated. This work presents a powerful biometric security system (BSS) to protect different biometric modalities such as faces, iris, and fingerprints. The proposed BSS model is based on hybridizing auto-encoder (AE) network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their secrecy. The employed AE network is unsupervised deep learning (DL) structure used in the proposed BSS model to extract main biometric features. These obtained features are utilized to generate two random chaos matrices. The first random chaos matrix is used to permute the pixels of biometric images. In contrast, the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional (2D) chaotic logistic map (CLM) algorithm. To assess the efficiency of the proposed BSS, (1) different standardized color and grayscale images of the examined fingerprint, faces, and iris biometrics were used (2) comprehensive security and recognition evaluation metrics were measured. The assessment results have proven the authentication and robustness superiority of the proposed BSS model compared to other existing BSS models. For example, the proposed BSS succeeds in getting a high area under the receiver operating characteristic (AROC) value that reached 99.97% and low rates of 0.00137, 0.00148, and 3516 CMC, 2023, vol.74, no.2 0.00157 for equal error rate (EER), false reject rate (FRR), and a false accept rate (FAR), respectively.

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A generic framework for deep incremental cancelable template generation

Cited by 11 publications

References 50 publications

Biometric Template Attacks and Recent Protection Mechanisms: A Survey

Biometric Template Attacks and Recent Protection Mechanisms: A Survey

Feature extraction and learning approaches for cancellable biometrics: A survey

Proposed Biometric Security System Based on Deep Learning and Chaos燗lgorithms

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