Cancelable Biometric Template Research Articles

Deep secure PalmNet: A novel cancelable palmprint template protection scheme with deep attention net and randomized hashing security mechanism

Overcoming challenges in the current cancelable palmprint template is essential for enhancing privacy and security in biometric systems, as these templates exhibit vulnerabilities in both template security and recognition performance. To address these challenges, we present a novel cancelable palmprint template protection scheme with a deep attention net and randomized hashing security mechanism. Firstly, to enhance recognition performance, we designed a deep attention net that is integrated into the model, aiming to improve its feature extraction ability. Additionally, in recognition of the paramount importance of security and privacy, we introduce a randomized hashing security mechanism. This mechanism, incorporating chaotic sequences as weights in a neuron activation layer, appends to the model and enables dynamic control of neuron activation while generating diverse palmprint feature templates. Subsequently, it enhances security and privacy by combining a Logistic-Tent-Sine (LTTS) random key with palmprint feature values through matrix multiplication. Furthermore, to optimize efficiency, particularly with a large dataset, a binarization layer is implemented using the Straight-Through Estimation (STE) algorithm. This layer contributes to computational efficiency and expedient data processing, further improving the performance of our palmprint template protection. The experimental results validate the outstanding accuracy of this scheme on TJU and PolyU palmprint datasets, establishing it as a state-of-the-art solution with remarkable recognition performance. Moreover, security analysis confirms its compliance with cancelable biometric template protection criteria, ensuring superior irreversibility, unlinkability, revocability, and privacy against various attacks.

Robust biometric scheme against replay attacks using one-time biometric templates

User authentication is an important issue on the Internet and usually solved through static and often unique passwords. Another method is to use biometrics, but biometric data are sensitive and need to be protected. Protection schemes such as cancelable biometric template generation have appeared, but they are sensitive to replay attacks. In this paper, we propose an original method to generate one-time biometric templates for user authentication applications. This proposed scheme limits replay attacks, consisting of an attacker maliciously retransmitting an intercepted user's identity proof. Our method is generic: any biometric modality could be used, the identity verification is realized by the service/identity provider to be realistic. Biometric features are extracted from captures using deep learning and then protected with biohashing, a cancelable biometric scheme. Finally, a step consisting of cryptographic hashing and symmetric encryption guarantees the generation of a one-time, non-replayable template. We have tested our scheme on two common biometric databases, from faces and fingerprints, and the results confirm its efficiency and robustness to attacks given a rigorous threat model.

The reversibility of cancelable biometric templates based on iterative perturbation stochastic approximation strategy

Researchers have been proposing different matching frameworks for cancelable template design to improve the matching performance and security of biometric authentication systems. Despite the advantages provided by these systems, they are often vulnerable to different attacks due to their templates' invertible nature. This study utilized an iterative perturbation stochastic approximation procedure to analyze the irreversible order-based encoding approach used in cancelable biometric template transformation. The strategy begins by exploiting the scores corresponding to the encoded words, then a mixed variable-based iterative perturbation is used to generate independent random elements and consequently the corresponding template from the scores. The strategy exploits the vulnerability of three different cancelable systems it has tested, demonstrating that other techniques of similar nature fall short of the security standard for attack mitigation that cancelable biometric systems require. After analyzing the reasons that exploit the system's vulnerability, we use a parameterized thresholding non-uniform quantization as a countermeasure to boost the system's robustness while maintaining a balanced performance-security trade-off. As a result, the system can evade attacks without significantly hindering its matching performance. Finally, generality and computational complexity evaluations on different modalities and strategies validate the attack's efficacy and realism, respectively.

Efficient Multi-Biometric Secure-Storage Scheme Based on Deep Learning and Crypto-Mapping Techniques

Cybersecurity has been one of the interesting research fields that attract researchers to investigate new approaches. One of the recent research trends in this field is cancelable biometric template generation, which depends on the storage of a cipher (cancelable) template instead of the original biometric template. This trend ensures the confidential and secure storage of the biometrics of a certain individual. This paper presents a cancelable multi-biometric system based on deep fusion and wavelet transformations. The deep fusion part is based on convolution (Conv.), convolution transpose (Conv.Trans.), and additional layers. In addition, the deployed wavelet transformations are based on both integer wavelet transforms (IWT) and discrete wavelet transforms (DWT). Moreover, a random kernel generation subsystem is proposed in this work. The proposed kernel generation method is based on chaotic map modalities, including the Baker map and modified logistic map. The proposed system is implemented on four biometric images, namely fingerprint, iris, face, and palm images. Furthermore, it is validated by comparison with other works in the literature. The comparison reveals that the proposed system shows superior performance regarding the quality of encryption and confidentiality of generated cancelable templates from the original input biometrics.

Optical Ciphering Scheme for Cancellable Speaker Identification System

Most current security and authentication systems are based on personal biometrics. The security problem is a major issue in the field of biometric systems. This is due to the use in databases of the original biometrics. Then biometrics will forever be lost if these databases are attacked. Protecting privacy is the most important goal of cancelable biometrics. In order to protect privacy, therefore, cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification databases. One methodology to achieve non-invertibility is the employment of non-invertible transforms. This work suggests an encryption process for cancellable speaker identification using a hybrid encryption system. This system includes the 3D Jigsaw transforms and Fractional Fourier Transform (FrFT). The proposed scheme is compared with the optical Double Random Phase Encoding (DRPE) encryption process. The evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure, authoritative, and feasible. The encryption and cancelability effects are good and reveal good performance. Also, it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.

Max-min threshold-based cancelable biometric templates for low-end devices

Biometrics are widely used in security systems, but it has drawbacks such as its nonreusability, being susceptible to getting stolen, and being prone to unauthorized access. These issues can be tackled using cancelable biometrics. In this work, features extracted using Log-Gabor filters are processed according to the proposed max-min thresholding. The resulting binary features are transformed by random projection on a key matrix to create a cancelable template. The method was carefully evaluated using publicly accessible datasets of face near-infrared, palm print, palm vein, dorsal vein, wrist vein, and knuckle print. A significant improvement in performance over many recent methods was observed. Also, the effectiveness of the generated cancelable templates was observed in the general, stolen token, and changeable scenarios. The generated template is 25% of the original input size, which makes it efficient for low-end devices.

CBRW: a novel approach for cancelable biometric template generation based on 1-D random walk.

Cancelable Biometric is a challenging research field in which security of an original biometric image is ensured by transforming the original biometric into another irreversible domain. Several approaches have been suggested in literature for generating cancelable biometric templates. In this paper, two novel and simple cancelable biometric template generation methods based on Random Walk (CBRW) have been proposed. By employing random walk and other steps given in the proposed two algorithms viz. CBRW-BitXOR and CBRW-BitCMP, the original biometric is transformed into a cancelable template. The performance of the proposed methods is compared with other state-of-the-art methods. Experiments have been performed on eight publicly available gray and color datasets i.e. CP (ear) (gray and color), UTIRIS (iris) (gray and color), ORL (face) (gray), IIT Delhi (iris) (gray and color), and AR (face) (color). Performance of the generated templates is measured in terms of Correlation Coefficient (Cr), Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), Mean Absolute Error (MAE), Number of Pixel Change Rate (NPCR), and Unified Average Changing Intensity (UACI). By experimental results, it has been proved that proposed methods are superior than other state-of-the-art methods in qualitative as well as quantitative analysis. Furthermore, CBRW performs better on both gray as well as color images.

A cancelable biometric identification scheme based on bloom filter and format-preserving encryption

Biometric based authentication systems are being prominently used everywhere. The biometric data, popularly known as a biometric template, is generally stored on the database server in its unprotected form. Unlike passwords, once compromised, biometric data can never be recovered. An ideal biometric system should provide accessibility, acceptability, availability, high security, and high biometric performance to the user. Current deployments generally relax in one or more requirements, resulting in lingering concerns about the privacy and security of individuals’ biometric data. Our proposed work introduces a cancelable biometric template protection scheme based on the format-preserving encryption and Bloom filters. The format-preserving encryption encrypts the biometric template, which then maps to the Bloom filter based template that represents the cancelable template. The use of format-preserving encryption along with Bloom filters helps to achieve the security of the input biometric template and identification with good recognition performance. We achieve 0.2% FRR at 0.01% FAR for IITD-CASIA virtual dataset in the uni-biometric scenario. A comparison with the existing schemes shows that our proposed scheme exhibits high recognition performance for both uni-biometric and multi-biometric datasets, while simultaneously, the security of the overall system is preserved.

Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last decades. It achieves safe access to systems using biometrics instead of the traditional passwords. The utilization of a biometric in its original format makes it usable only once. Therefore, a cancelable biometric template should be used, so that it can be replaced when it is attacked. Cancelable biometrics aims to enhance the security and privacy of biometric authentication. Digital encryption is an efficient technique to be used in order to generate cancelable biometric templates. In this paper, a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification systems. The considered biometric in this paper is the speech signal. The speech signal is transformed into its spectrogram. Then, the spectrogram is encrypted using two cascaded optical encryption algorithms. The first algorithm is the Optical Scanning Holography (OSH) for its efficiency as an encryption tool. The OSH encrypted spectrogram is encrypted using Double Random Phase Encoding (DRPE) by implementing two Random Phase Masks (RPMs). After the two cascaded optical encryption algorithms, the cancelable template is obtained. The verification is implemented through correlation estimation between enrolled and test templates in their encrypted format. If the correlation value is larger than a threshold value, the user is authorized. The threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two curves. The implementation of optical encryption is adopted using its software rather than the optical setup. The efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation results. It can improve the biometric data security without deteriorating the recognition accuracy. Simulation results give close-to-zero values for the Equal Error Rate (EER) and close-to-one values for the Area under Receiver Operator Characteristic (AROC) curve.

Cancelable Multi-biometric Template Generation Based on Dual-Tree Complex Wavelet Transform

In this article, we introduce a new cancelable biometric template generation layout depending on selective encryption technology and Dual-Tree Complex Wavelet Transform (DT-CWT) fusion. The input face biometric is entered into the automatic face-segmentation (Viola-Jones) algorithm to detect the object in a short time. Viola-Jones algorithm can detect the left eye, right eye, nose, and mouth of the input biometric image. The encoder can choose the left or right eye to generate a cancelable biometric template. The selected eye image of size M × N is XORed with the created pseudo-random number (PRN) matrix CM × N to provide an initial primary image. Arnold Cat Map (ACM) is used to scramble the PRN matri pixel by pixel for primary image encryption keeping the pixel values themselves unchanged. Finally, the ACM scrambled eye images are encrypted using Advanced Encryption Standard (AES) algorithm for database storage. Moreover, the PRN and Initial Key (IK) of the AES algorithm for the same person are used for the biometric model authentication process for database storage. The IK is generated from the right finger and right eye fusion using DT-CWT technique. To avoid injury of a single eye, both right-eye and left-eye models are developed.

CBRC: a novel approach for cancelable biometric template generation using random permutation and Chinese Remainder Theorem

CBRC: a novel approach for cancelable biometric template generation using random permutation and Chinese Remainder Theorem

Random area-perimeter method for generation of unimodal and multimodal cancelable biometric templates

In recent times, biometric based authentication systems have seen a tremendous growth in various applications. However, if databases in multiple applications are created using the same biometric characteristic and algorithm, then any compromise of the stored template in one biometric system may jeopardise the security of the other biometric systems as well. More importantly, such a compromise may also lead to a permanent loss of the biometric characteristic. Therefore, the cancelability or revocability of biometrics has become quite an essential requirement. In this paper, a novel scheme, the Random Area & Perimeter Method (RAPM)) is presented in which a biometric characteristic of an individual is transformed into random values which are stored as cancelable biometric templates. The proposed scheme computes area and perimeter of the Bezier curve which are obtained through interpolation of feature points of original biometrics and a random point chosen by the user. The area and perimeter thus computed exhibit pseudo-random properties. This technique outperforms many existing techniques. Moreover, a dimensionality reduction to the tune of more than 95% has been obtained without compromising the matching performance. The average values obtained for EER, DI and RI are 0.0045, 6.28 and 99.64 respectively. These values are better than those obtained from the available state of the art approaches. The proposed scheme has also been analysed for various privacy issues like non-invertibility, revocability, and unlinkability.

Cancelable Iris template for secure authentication based on random projection and double random phase encoding

Biometric recognition approaches have overwhelmed major issues in traditional authentication systems. Privacy invasion and security are the needed features in the effective development of biometric recognition systems. Recently some biometric techniques have been suffered to hacking trails also. Hence there is a despairing need to propose a novel cancelable iris recognition scheme. The prime objective of this research is to create a transformed cancelable biometric template through an encryption function and a one-way transformation function. The random projection matrix is employed here to generate the first feature vector. Then an encrypted cancelable iris code is generated using Double Random Phase Encryption (DRPE) in the Fractional Fourier Transform (FFT) domain. The proposed framework utilizes both the left and right iris image of a person and generates a single cancelable iris template. This feature guarantees the privacy-preserving cancelable iris code generation for secure authentication. The experiment is carried on two state-of-art datasets CASIA Iris V4 and IITD iris to affirm the efficacy of the proposed methodology. The result analysis witnessed a promising accuracy of 99.59%, a recognition rate of 99.88% with a lower Equal Error Rate (ERR) of 0.46%. It has also been proved that the proposed approach is computationally efficient as it recognizes the iris code with less recognition time of 7 ms with a maximum true positive and true negative rate of 100%.

Efficient and Secure Cancelable Biometric Authentication Framework Based on Genetic Encryption Algorithm

Various cancelable biometric techniques have been proposed to maintain user data security. In this work, a cancelable biometric framework is introduced to satisfy user data security and keeping the original biometric template safe away from intruders. Thus, our main contribution is presenting a novel authentication framework based on the evolutionary Genetic Algorithm (GA)-based encryption technique. The suggested framework produces an entirely unrecognized biometric template by hiding the whole discriminative features of biometric templates; this is with exploiting the outstanding characteristics of the employed Genetic operations of the utilized encryption technique. Firstly, the GA initiates its search from a population of templates, not a single template. Secondly, some statistical operators are used to exploit the resulting initial population to generate successive populations. Finally, the crossover and mutation operations are performed to produce the ultimate cancelable biometric templates. Different biometric databases of the face and fingerprint templates are tested and analyzed. The proposed cancelable biometric framework achieves appreciated sensitivity and specificity results compared to the conventional OSH (Optical Scanning Holography) algorithm. It accomplishes recommended outcomes in terms of the AROC (Area under the Receiver Operating Characteristic) and the probability correlation distribution between the original biometrics and the encrypted biometrics stored in the database. The experimental results prove that the proposed framework achieves excellent results even if the biometric system suffers from different noise ratios. The proposed framework achieves an average AROC value of 0.9998, an EER (Equal Error Rate) of 2.0243×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , FAR (False Acceptance Rate) of 4.8843×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , and FRR (False Rejection Rate) of 2.2693×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> .

Rebuttal to “Comments on Random Distance Method Generating Unimodal and Multimodal Cancelable Biometric Features”

This rebuttal highlights major flaws in the comments made by Lamba due to possible oversight in understanding the scheme given by Kaur and Khanna. Firstly, the random distance method (RDM) does not suffer from any mathematical fallacy. It is a unique technique to generate cancelable biometric templates. Secondly, the size of the original log-Gabor feature vector is reduced by half due to the inherent nature of the RDM scheme, and it does not affect the inter-and intra user variations in any adverse way. An experimental analysis is also performed to showcase that RDM maintains inter and intra-user variations. Thirdly, the comment correspondence author has not noticed that the sole purpose of using the random grid (RG) with OR operation is to increase the entropy of the log-Gabor features, which have a low dynamic range. The issue of partial information revelation is out of context here as salting is not used for hiding information. Finally, the author of comment correspondence has failed to understand that padding is used in the implementation of median filters, which is very common in the filtering process.

Discrete Transforms and Matrix Rotation Based Cancelable Face and Fingerprint Recognition for Biometric Security Applications.

The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.

Adaptive Weighted Graph Approach to Generate Multimodal Cancelable Biometric Templates

Multimodal biometric systems offer numerous advantages over unimodal counterparts and are being used extensively in diverse applications. However, fusion of biometric data is a non-trivial task and curtail employability of multimodal systems for a varying set of biometric characteristics with different type and dimension. Moreover, comprehensive solutions against adversary attacks that ensure template protection and prevent presentation attacks are not in place. In this article, a secure multimodal cancelable biometric system is proposed to address these concerns. This approach introduces key images based generic feature extraction technique which reduces feature dimension and achieves revocability. The non-invertibility and unlinkability are ensured through cross-diffusion of complementary information from different modalities. A new feature fusion method based on an adaptive graph is proposed to generate multimodal cancelable biometric templates. Robustness against presentation attack is accomplished through quality based adaptation of features. Extensive experimentation is performed on benchmark databases for fingerprint, face, and iris, to illustrate the efficacy of multimodal cancelable templates. The proposed approach is shown to perform favorably against state-of-the-art feature fusion methods. Furthermore, the resilience of the proposed approach against security and privacy attacks is demonstrated.

RP-LPP : a random permutation based locality preserving projection for cancelable biometric recognition

Biometrics are being increasingly used across the world, but it also raises privacy and security concerns of the enrolled identities. The main reason is due to the fact that biometrics are not cancelable and if compromised may give access to the intruder. Cancelable biometric template is a solution to this problem which can be reissued if compromised. In this paper, we suggest a simple and powerful method called Random Permutation Locality Preserving Projection (RP-LPP) for Cancelable Biometric Recognition. Here, we exploit the mathematical relationship between the eigenvalues and eigenvectors of the original biometric image and its randomly permuted version is exploited for carrying out cancelable biometric recognition. The proposed technique work in a cryptic manner by accepting the cancelable biometric template and a key (called PIN) issued to a user. The effectiveness of the proposed techniques is demonstrated on three freely available face (ORL), iris (UBIRIS) and ear (IITD) datasets against state-of-the-art methods. The advantages of proposed technique are (i) the classification accuracy remains unaffected due to cancelable biometric templates generated using random permutation, (ii) security and quality of generated templates and (iii) robustness across different biometrics. In addition, no image registration is required for performing recognition.

Partial DCT-based cancelable biometric authentication with security and privacy preservation for IoT applications

Biometric authentication is being vastly used in identity verification for several IoT applications, nowadays. The security and privacy of the biometrics templates used in the authentication process becomes mandatory as it contains rich personal information of the user. Cancelable Biometric System (CBS) is a template securing approach using repeated distortions/transformations at feature/signal level. It is an effective template securing approach which provides non-invertibility, revocability and diversity to the users enrolled with the cancelable template database. The cancelable template database containing cancelable biometric templates of the users is typically stored in a trusted standalone server. To meet the growing demand of IoT applications, cloud computing with provision to store and process even large database, is being largely deployed. However, the security and privacy of the data stored in the cloud is uncertain. In this paper, we have proposed a novel partial DCT-based CBS with privacy and security preservation for providing authentication services in various IoT applications. We have also devised techniques for session key agreement, data encryption and data integrity in the proposed framework to solve the issues of confidentiality, integrity, and availability (CIA) during cancelable template enrolment and authentication. The experimental results and analysis prove that proposed approach performs user authentication with high accuracy and minimal overhead while preserving security and privacy of sensitive cancelable biometric templates.

Random Distance Method for Generating Unimodal and Multimodal Cancelable Biometric Features

The cancelable biometric-based template protection method proposed in this paper addresses security and privacy concerns emerging from the phenomenal usage of biometric systems. Cancelable biometric transforms the original biometric identity of a user to a pseudo-biometric identity that is used for storage and matching purposes. The use of pseudo-identity mitigates privacy risks and allows revocability in case of compromise. This paper proposes a novel template transformation technique named random distance method which not only generates discriminative and privacy preserving revocable pseudo-biometric identities, but also reduces their size by 50%. Extensive experimentation is performed to analyze recognition and protection performance on unimodal and multimodal pseudo-identities generated with various biometric modalities such as face, thermal face, palmprint, palmvein, and fingervein. It is observed that the matching performance obtained with the proposed cancelable templates in the worst-case is closer to the performance achieved in the original domain. Also, multimodal cancelable biometric templates generated with the proposed method are observed for improved performance. Furthermore, the proposed approach is successfully analyzed for non-invertibilty, unlinkability, as well as its resistance for various types of attacks like attacks via record multiplicity, dictionary, false accepts, and brute force.