A linkable signature scheme supporting batch verification for privacy protection in crowd-sensing

The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network,so it has promoted the applications of crowd-sensing services in different fields,but also brings more privacy security challenges,the most commom which is privacy leakage.As a privacy protection technology combining data integrity check and identity anonymity,ring signature is widely used in the field of privacy protection.However,introducing signature technology leads to additional signature verification overhead.In the scenario of crowd-sensing,the existing signature schemes have low efficiency in multi-signature verification.Therefore,it is necessary to design an efficient multi-signature verification scheme while ensuring security.In this paper,a batch-verifiable signature scheme is proposed based on the crowd-sensing background,which supports the sensing platform to verify the uploaded multiple signature data efficiently,so as to overcoming the defects of the traditional signature scheme in multi-signature verification.In our proposal,a method for linking homologous data was presented,which was valuable for incentive mechanism and data analysis.Simulation results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

Dynamic Signature Verification Using Pattern Recognition

Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.

A New Algorithm for On-Line Handwriting Signature Verification Based on Evolutionary Computation

The paper proposes an on-line signature verification algorithm, through which test sample and template signatures can be optimizedly matched, based on evolutionary computation （EC）. Firstly, the similarity of signature curve segment is defined, and shift and scale transforms are also introduced due to the randoness of on-line signature. Secondly, this paper puts forward signature verification matching algorithm after establishment of the mathematical model. Thirdly, the concrete realization of the algorithm based on EC is discussed as well. In addition, the influence of shift and scale on the matching result is fully considered in the algorithm. Finally, a computation example is given, and the matching results between the test sample curve and the template signature curve are analyzed in detail, The preliminary experiments reveal that the type of signature verification problem can be solved by EC.

Multi-proxy quantum group signature scheme with threshold shared verification

A multi-proxy quantum group signature scheme with threshold shared verification is proposed. An original signer may authorize a proxy group as his proxy agent. Then only the cooperation of all the signers in the proxy group can generate the proxy signature on behalf of the original signer. In the scheme, any t or more of n receivers can verify the message and any t - 1 or fewer receivers cannot verify the validity of the proxy signature.

IMPROVEMENT OF NONREPUDIABLE THRESHOLD MULTI-PROXY THRESHOLD MULTI-SIGNATURE SCHEME WITH SHARED VERIFICATION

In 2005, Bao, et al. [Appl. Math. and Comput., vol.169, No.2, 2005] showed that Tzeng, et al.’s nonrepudiable threshold multi-proxy multi-signature scheme with shared verification was insecure, and proposed an improved scheme with no Share Distribution Center (SDC). This paper shows that Bao, et al.’s scheme suffers from the proxy relationship inversion attack and forgery attack, and pro- poses an improvement of Bao, et al.’s scheme.

Quantum Homomorphic Signature with Repeatable Verification

In January 2015,the first quantum homomorphic signature scheme was proposed creatively.However,only one verifier is allowed to verify a signature once in this scheme.In order to support repeatable verification for general scenario,we propose a new quantum homomorphic signature scheme with repeatable verification by introducing serial verification model and parallel verification model.Serial verification model solves the problem of signature verification by combining key distribution and Bell measurement.Parallel verification model solves the problem of signature duplication by logically treating one particle of an EPR pair as a quantum signature and physically preparing a new EPR pair.These models will be beneficial to the signature verification of general scenarios.Scheme analysis shows that both intermediate verifiers and terminal verifiers can successfully verify signatures in the same operation with fewer resource consumption,and especially the verified signature in entangled states can be used repeatedly.

OffSig-SinGAN: A Deep Learning-Based Image Augmentation Model for Offline Signature Verification

Offline signature verification(OfSV)is essential in preventing the falsification of documents.Deep learning(DL)based OfSVs require a high number of signature images to attain acceptable performance.However,a limited number of signature samples are available to train these models in a real-world scenario.Several researchers have proposed models to augment new signature images by applying various transformations.Others,on the other hand,have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples.Hence,augmenting a sufficient number of signatures with variations is still a challenging task.This study proposed OffSig-SinGAN:a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature verification.The proposed model is capable of augmenting better quality signatures with diversity from a single signature image only.It is empirically evaluated on widely used public datasets;GPDSsyntheticSignature.The quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference,peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and frechet inception distance(FID).Furthermore,various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy rate.Experiment results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation methods.The improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.

Neutrosophic Rule-Based Identity Verification System Based on Handwritten Dynamic Signature Analysis

Identity verification using authenticity evaluation of handwritten signatures is an important issue.There have been several approaches for the verification of signatures using dynamics of the signing process.Most of these approaches extract only global characteristics.With the aim of capturing both dynamic global and local features,this paper introduces a novel model for verifying handwritten dynamic signatures using neutrosophic rule-based verification system(NRVS)and Genetic NRVS(GNRVS)models.The neutrosophic Logic is structured to reflect multiple types of knowledge and relations among all features using three values:truth,indeterminacy,and falsity.These three values are determined by neutrosophic membership functions.The proposed model also is able to deal with all features without the need to select from them.In the GNRVS model,the neutrosophic rules are automatically chosen by Genetic Algorithms.The performance of the proposed system is tested on the MCYT-Signature-100 dataset.In terms of the accuracy,average error rate,false acceptance rate,and false rejection rate,the experimental results indicate that the proposed model has a significant advantage compared to different well-known models.

One-Class Arabic Signature Verification: A Progressive Fusion of Optimal Features

Signature verification is regarded as the most beneficial behavioral characteristic-based biometric feature in security and fraud protection.It is also a popular biometric authentication technology in forensic and commercial transactions due to its various advantages,including noninvasiveness,user-friendliness,and social and legal acceptability.According to the literature,extensive research has been conducted on signature verification systems in a variety of languages,including English,Hindi,Bangla,and Chinese.However,the Arabic Offline Signature Verification(OSV)system is still a challenging issue that has not been investigated as much by researchers due to the Arabic script being distinguished by changing letter shapes,diacritics,ligatures,and overlapping,making verification more difficult.Recently,signature verification systems have shown promising results for recognizing signatures that are genuine or forgeries;however,performance on skilled forgery detection is still unsatisfactory.Most existing methods require many learning samples to improve verification accuracy,which is a major drawback because the number of available signature samples is often limited in the practical application of signature verification systems.This study addresses these issues by presenting an OSV system based on multifeature fusion and discriminant feature selection using a genetic algorithm(GA).In contrast to existing methods,which use multiclass learning approaches,this study uses a oneclass learning strategy to address imbalanced signature data in the practical application of a signature verification system.The proposed approach is tested on three signature databases(SID)-Arabic handwriting signatures,CEDAR(Center of Excellence for Document Analysis and Recognition),and UTSIG(University of Tehran Persian Signature),and experimental results show that the proposed system outperforms existing systems in terms of reducing the False Acceptance Rate(FAR),False Rejection Rate(FRR),and Equal Error Rate(ERR).The proposed system achieved 5%improvement.

Reference Selection for Offline Hybrid Siamese Signature Verification Systems

This paper presents an off-line handwritten signature verification system based on the Siamese network,where a hybrid architecture is used.The Residual neural Network(ResNet)is used to realize a powerful feature extraction model such that Writer Independent(WI)features can be effectively learned.A single-layer Siamese Neural Network(NN)is used to realize a Writer Dependent(WD)classifier such that the storage space can be minimized.For the purpose of reducing the impact of the high intraclass variability of the signature and ensuring that the Siamese network can learn more effectively,we propose a method of selecting a reference signature as one of the inputs for the Siamese network.To take full advantage of the reference signature,we modify the conventional contrastive loss function to enhance the accuracy.By using the proposed techniques,the accuracy of the system can be increased by 5.9%.Based on the GPDS signature dataset,the proposed system is able to achieve an accuracy of 94.61%which is better than the accuracy achieved by the current state-of-the-art work.

Multi-Path Attention Inverse Discrimination Network for Offline Signature Verification

Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business envir-onments.However,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective information.Second,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass differences.These challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective information.To solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature images.Moreover,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature images.In addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of strokes.We respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification methods.This is more helpful to the task of signature authentication in justice and finance.

Eliminating Forgers Based on Intra Trial Variability in Online Signature Verification Using Handglove and Photometric Signals

The novel reinforcement to the data glove based dynamic signature verification system, using the Photometric measurement values collected simultaneously from photo plethysmography (PPG) during the signing process is the emerging technology. Skilled forgers try to attempt the genuine signatures in many numbers of trials. The wide gap in the Euclidian distances between forgers and the genuine template features prohibits them from successful forging. This has been proved by our repeated experiments on various subjects using the above combinational features. In addition the intra trial features captured during the forge attempts also differs widely in the case of forgers and are not consistent that of a genuine signature. This is caused by the pulse characteristics and degree of bilateral hand dimensional similarity, and the degrees of pulse delay. Since this economical and simple optical-based technology is offering an improved biometric security, it is essential to look for other reinforcements such the variability factor considerations which we proved of worth considering.

Signature Verification for Multiuser Online Kanji Learning System

Multiuser online system is useful, but the administrator must be nervous at security problem. To solve this problem, the authors propose applying signature verification to multiuser online system. At the authors＇ research, they attempt adding signature verification function based on DP （Dynamic Programming） matching to existing multiuser online kanji learning system. In this paper, the authors propose the construction of the advance system and methods of signature verification, and evaluate performance of those signature verification methods that difference is combination of using features. From signature verification＇s experimental results, the authors adopted to use writing velocity and writing speed differential as using feature to verify the writer for the system. By using signature database which is construct with 20 genuine signatures and 20 forged signatures with 40 writers and written mostly by English or Chinese literal, experimental results of signature verification records 12.71% as maximum EER （Equal Error Rate）, 6.00% as minimum EER, and 8.22% as average EER. From mentioned above, the authors realized to advance the reliability and usefulness of the multiuser online kanji learning system.

Fusion of Type-2 Neutrosophic Similarity Measure in Signatures Verification Systems: A New Forensic Document Analysis Paradigm

Signature verification involves vague situations in which a signature could resemble many reference samples ormight differ because of handwriting variances. By presenting the features and similarity score of signatures from thematching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy,a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertaintiesand ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values,which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1neutrosophic representation is also unable to adjust to various degrees of uncertainty. The proposed work exploresthe type-2 neutrosophic logic to enable additional flexibility and granularity in handling ambiguity, indeterminacy,and uncertainty, hence improving the accuracy of signature verification systems. Because type-2 neutrosophiclogic allows the assessment of many sources of ambiguity and conflicting information, decision-making is moreflexible. These experimental results show the possible benefits of using a type-2 neutrosophic engine for signatureverification by demonstrating its superior handling of uncertainty and variability over type-1, which eventuallyresults in more accurate False Rejection Rate (FRR) and False Acceptance Rate (FAR) verification results. In acomparison analysis using a benchmark dataset of handwritten signatures, the type-2 neutrosophic similaritymeasure yields a better accuracy rate of 98% than the type-1 95%.

Authentication based on feature of hand-written signature

The typical features of the coordinate and the curvature as well as the recorded time information were analyzed in the hand-written signatures.In the hand-written signature process 10 biometric features were summarized:the amount of zero speed in direction x and direction y,the amount of zero acceleration in direction x and direction y,the total time of the hand-written signatures,the total distance of the pen traveling in the hand-written process,the frequency for lifting the pen,the time for lifting the pen,the amount of the pressure higher or lower than the threshold values.The formulae of biometric features extraction were summarized.The Gauss function was used to draw the typical information from the above-mentioned biometric features,with which to establish the hidden Markov mode and to train it.The frame of double authentication was proposed by combing the signature with the digital signature.Web service technology was applied in the system to ensure the security of data transmission.The training practice indicates that the hand-written signature verification can satisfy the needs from the office automation systems.

Threshold proxy quantum signature scheme with threshold shared verification

A threshold proxy quantum signature scheme with threshold shared verification is proposed. An original signer could authorize a group as its proxy signers. Then only t or more of n persons in the proxy group can generate the proxy signature on behalf of the original signer and any t-1 or fewer ones cannot do that. When the proxy signature needs to be verified,any t or more of n persons belonging to the verification group can verify the message and any t-1 or fewer ones cannot verify the validity of the proxy signature.

Lattice-based sequential aggregate signatures with lazy verification

This paper proposes the first lattice-based sequential aggregate signature （SAS） scheme with lazy verification that is provably secure in the random oracle model. As opposed to large integer factoring and discrete logarithm based systems, the security of the construction relies on worst-case lattice problem, namely, under the small integer solution （SIS） assumption. Generally speaking, SAS schemes enable any group of signers ordered in a chain to sequentially combine their signatures such that the size of the aggregate signature is much smaller than the total size of all individual signatures. Unlike prior such proposals, the new scheme does not require a signer to retrieve the keys of other signers and verify the aggregate-so-far before adding its own signature, and the signer can add its own signature to an unverified aggregate and forward it along immediately, postponing verification until load permits or the necessary public keys are obtained. Indeed, the new scheme does not even require a signer to know the public keys of other signers.

A New Signature Scheme with Shared Verification

With expanding user demands, digital signature techniques are also being expanded greatly, from single signature and single verification techniques to techniques supporting multi-users. This paper presents a new digital signature scheme with shared verification based on the fiat-shamir signature scheme. This scheme is suitable not only for digital signatures of one public key, but also for situations where multiple public keys are required. In addition, the scheme can resist all kinds of collusion, making it more practicable and safer. Additionally it is more efficient than other schemes.

A Proxy Blind Signature Scheme and an Off-Line Electronic Cash Scheme

To support withdrawing and storing money from all levels of the bank for the customers in the real world, in this paper, we propose a proxy blind signature scheme and an off-line e-cash scheme based on the new proxy blind signature scheme. The pro- posed proxy blind signature is proven secure in the random oracle model under chosen-target computational Diffie-Hellman assump- tions, and the e-cash scheme can satisfy the security requirements of unforgeability, anonymity, and traceability.