Signature Verification Process Research Articles

Using a smart contract for the floral supply chain

The floral supply chain is one of the most delicate logistics networks in existence, requiring intensive monitoring of temperature, humidity, and product conditions to ensure the quality of flowers. However, the traditional centralized supply chain involves exchanging information back and forth, and therefore wastes time, money, and resources. In addition, centralized data control without security verification exposes logistics information to the risk of hacking, information tampering, and counterfeiting.Blockchain technology offers decentralization, immutability, and encryption security. Furthermore, application of Internet of Things (IoT) technology has made it easier to monitor goods and the distribution process. Combining the above two new technologies can effectively improve the transparency and traceability of the floral supply chain and make transactions more secure through the digital signature verification process. In this research, we propose a smart contract for the floral supply chain. Through contract compilation, the proposed contract defines the delivery process status, delivery specifications, and other conditions in the shipping process so that the status of the goods is updated on the blockchain, giving the supply chain higher traceability. This study also defines the process for and conditions of money transactions between buyers and sellers using such smart contracts, thereby automating the transaction, improving its efficiency, and saving on labor costs and paper waste. This research also compares the proposed smart contract to traditional transaction methods. The pros and cons are thoroughly discussed, offering significant benefit to decision makers.

Developing a Model for Offline Signature Verification Using CNN Architectures and Genetic Algorithm

The signature verification process has many applications, such as its use in financial operations, providing the electronic signature of documents, and providing an additional confidentiality standard to verify the identity of users in computer systems. This process has the advantage of being accepted by the community and is less intrusive compared to other biological methods. Deep learning (DL) and CNN (Convolutional Neural Network) are widely used by bioinformaticians. Due to the difficulty in extracting features in other systems or models, DL and CNN-based signature verification systems have been significantly improved. Yet Hyperparameter optimization for CNN models remains a challenging problem in designing highly efficient models with the most accurate results. It is often convolutional neural network (CNN) models that are manually designed. The proposed method is focused on a genetic algorithm that develops a population of CNN models in order to find the best fit architecture for designing an offline signature verification model. Our model is tested on more than one dataset, BHSig260-Bengali, BHSig260-Hindiin, GPDS, and CEDAR. The result of the approach proposed in this paper has the highest discrimination rate of FRR of 2.5, FAR 3.2, EER 2.35, and 97.73 %-accuracy rate.

Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards

The development of quantum computing systems poses a great threat to the security of existing public key-based systems. As a result, the National Institute of Standards and Technology (NIST) started a Post-Quantum Cryptography (PQC) standardization project in 2015, and currently active research is being conducted to apply PQC to various cryptographic protocols. Unlike elliptic curve cryptography (ECC)-based schemes, PQC requires a large memory footprint and key/signature size. Therefore, when migrating PQC to a protocol, depending on the PQC and protocol specifications, it can be hard to migrate PQC. In the case of the WAVE protocol, it is difficult to satisfy the accuracy of a specific PQC algorithm because segmentation of the signature occurs during transmission due to the limitation of the maximum packet size. Therefore, in this paper, we present two methodologies that can apply PQC while complying with IEEE 1609.2 standards to the WAVE protocol in the V2V environment. Whereas previous migration studies have focused on designing a hybrid mode of protocols, this paper explores solutions more intuitively at the application layer of protocols. We analyzed two postquantum digital signature algorithms (Crystals-Dilithium and Falcon) and the structure of basic-safety messages (BSMs) of the V2V protocol on the size side. Through this, we propose methods that can perform an independent signature verification process without waiting for all divided signatures in the WAVE protocol. Our methodology overcomes the limitation that schemes with large signature sizes cannot be mounted into the WAVE protocol. We also note that the architecture used as an on-board unit (OBU) in an autonomous driving environment is mainly a microprocessor. We investigated an optimized PQC implementation in the OBU environment and simulated our methodology with the V2Verifier. Finally, we measured the accurate latency through simulation in Jetson Xavier, which is mainly used as an OBU in the V2V communication network.

Verification System for Handwritten Signatures with Modular Neural Networks

Handwritten signature is considered as one of the primary biometric processes for human verification in various applications including banking and legal documentations. In general, the handwritten signatures are verified with respect to the pressure, direction and speed followed on a plain document. However, the traditional methods of verification are less accurate and time consuming. The proposed work aims to develop a deep learning -based approach for handwritten signature verification process through a Modular Neural Network algorithm. The work utilized the handwritten signatures dataset downloaded from the kaggle website that consists of original and forged signatures of 30 individuals. The work also included a set of 20 individual signatures for improving the sample count on training and verification process.

An efficient and secure data sharing scheme for cloud data using hash based quadraplet wavelet permuted cryptography approach

SummaryEnsuring the reliable and secure data transmission in cloud systems is very essential and demanding task in the recent decades. For that purpose, various security frameworks have been deployed in the existing works, which are mainly focusing the improving the privacy and confidentiality of cloud data against the unauthenticated users. Yet, it facing the major problems of inefficient accessing, increased time consumption, computational complexity, storage complexity, and memory utilization rate. Hence, this research work intends to develop an advanced and efficient cryptography model, named as, hash based quadraplet wavelet permuted cryptography (HQWPC) for the secured cloud data storage and retrieval operations. Here, the bilinear mapping and group hash function generation processes are performed to generate the keys used for cryptographic operations. Also, the zig‐zag scanning, wavelet coefficients extraction, and permutation processes are accomplished for data encryption. Consequently, the inverse of these operations is performed while decrypting the data. In addition to that, an integrated signature control policy authentication mechanism is employed for validating the authenticity of the cloud data users. This kind of signature verification process could efficiently increase the security level of cloud data. For validating the performance of the proposed security framework, various evaluation metrics have been utilized during analysis. Then, the obtained results are compared with the recent state‐of‐the‐art models for proving the efficiency of the proposed technique over the other techniques.

A post-quantum lattice based lightweight authentication and code-based hybrid encryption scheme for IoT devices

The Internet of Things (IoT) introduces an active connection between smart devices for revolutionizing our modern lives in this world. But, IoT devices often exhibit several security issues, so transmission between the nodes should be protected using cryptographic approaches. However, the complexity of conventional cryptographic approaches is very high and is vulnerable to quantum attacks. This paper presents a robust and lightweight post-quantum lattice-based authentication and code-based hybrid encryption scheme for resource-constrained IoT devices. The proposed Ring-Learning with Errors (Ring-LWE) based authentication scheme introduces Bernstein reconstruction in polynomial multiplication to achieve minimal computation cost; hence, resource-limited IoT devices are viable to use the reliable authentication mutually. This approach offers indefinite identity privacy and location privacy. Hence, the proposed signature generation and verification process are highly efficient compared to the existing ring signature systems. Also, the proposed post-quantum hybrid code-based encryption scheme follows Diagonal Structure Based QC‑LDPC Codes with column loop optimization and Simplified Log Domain Sum-Product Algorithm (SLDSPA) to provide the function of light weight encryption with minimum hardware requirements. The total authentication delay of the proposed authentication scheme is 23% less than the authentication scheme that is considered conventional polynomial multiplication. Also, the optimized design of the proposed code based HE uses only 64 slices and 640 slices on Xilinx Virtex-6 FPGA for encoding and decoding processes, respectively. These simulation results prove the effectiveness of the proposed cryptographic scheme against other competitive systems in terms of its functionality and hardware complexities.

Performance Analysis of Deep Learning Approaches for Offline Signature Verification

Signature verification plays a significant role in many biometric authentication places. Many financial institutes require a robust signature verification process for check clearance, loan sanction, processing, pension relation documents, etc. Expert forgeries make it hard to authenticate an individual's identification based on signatures. Typically, this occurs when the forger understands the user's intricate features of the signature and strives to mimic it. Online signature verification approaches can extract various features such as keystrokes, pressure of the pointer, duration between the strokes and the lettering styles, so that verification becomes effective. However, the lack of these intricate details in offline signature, the authentication process becomes much more difficult. To address these issues, in this paper we propose deep learning-based approaches for offline signature verification. In this regard, we have used ZFNet, LeNet and AlexNet architectures with CEDAR, BHSig20 and UTsig datasets for our extensive. experimentation. We propose a learning model in which the dataset consists of multiple genuine and forged signatures. Further, performance analysis of these techniques has been carried out. It was found that LeNet has provided better training and testing accuracy with above 82% performance.

A Lightweight Authentication with Dynamic Batch-Based Group Key Management Using LSTM in VANET

Due to its complexity and mobility, VANET (vehicle ad hoc network) security has long plagued the development of the IoT industry. It is still a big challenge for users to decide the trustworthiness of an anonymous message or the preservation of personal information. Group signature is widely used in VANET anonymous authentication, but the existing solutions suffer from high computation costs in certificate revocation list (CRL) checking and signature verification process. In our scheme, we develop a lightweight protocol based on hashing functions and group keys, which escapes from the heavy computation cost. Then, we propose a dynamic batch-based group key distribution process, which is based on long short-term memory (LSTM) neural network to predict traffic flow and calculate the weight to determine the right time for key update. In this way, our method will significantly reduce computation delay and communication overhead. The security and performance analyses show that our scheme is more efficient in terms of authentication speed while keeping conditional privacy in VANET.

Siamese Sinir Ağı One-Shot Öğrenmeyi Kullanarak İmza Doğrulama

With the acceleration of digitalization in all areas of our lives, the need for biometric verification methods is increasing. The fact that biometric data is unique and biometric verification is stronger against phishing attacks compared to password-based authentication methods, has increased its preference rate. Signature verification, which is one of the biometric verification types, plays an important role in many areas such as banking systems, administrative and judicial applications. There are 2 types of signature verification, online and offline, for identifying the identity of the person and detecting signature forgery. Online signature verification is carried out during signing and temporal dynamic data are available regarding the person's signature. Offline verification is applied by scanning the image after signing, and this verification is limited to spatial data. Therefore, the offline signature verification process is considered a more challenging task. In this study, offline signature verification, independent of the writer, based on One-Shot Learning, was performed using Siamese Neural Network. Due to the fact that the Deep Convolution Neural Network requires a large amount of labeled data for image classification, real and fake signature distinction has been achieved by using the One-Shot Learning method, which can perform a successful classification by using less numbers of signature images. As a result of the experiments conducted on signature datasets, using the Siamese architecture, the proposed approach achieved percentage accuracy of 93.23, 92.11, 89.78, 91.35 verification in 4NSigComp2012, SigComp2011, 4NSigComp2010 and BHsig260 respectively.

A new wrapper feature selection method for language-invariant offline signature verification

Among various biometric systems, an offline signature verification system has been widely used in all fields such as in banks, educational institutes, legal procedures and, criminal investigation where authentication and verification are utmost required. Despite the popularity of the online signature verification system, its offline counterpart still has great importance in developing countries, especially in rural areas, where easy availability of smart devices along with fast internet connection is not available. In this work, we have developed a language invariant offline signature verification model which is almost equally applicable for both writer dependent and writer independent scenarios. At first, an offline signature is collected as an image, following which a corresponding signal is generated using singular value decomposition. Then four different kinds of features namely, statistical, shape-based, similarity-based, and frequency-based are extracted from the transformed signal of the signature image. Next, to reduce the feature dimension, we have designed a novel wrapper feature selection method based on Red Deer Algorithm, a recently proposed meta-heuristic method, to keep only the relevant features to be used during signature authentication and verification process. Finally, a confidence score from the Naïve Bayes classifier has been used to perform the authentication and verification process. Our model has been evaluated on CEDAR (English), UTSig (Persian), Sigcomp 2011 Dutch, Sigcomp 2011 Chinese, and SigWIcomp 2015 Bengali signature datasets. Obtained results confirm that the proposed model can outperform many of its predecessors.

A Review - Signature Verification System Using Deep Learning: A Challenging Problem

One of the challenging and effective way of identifying person through biometric techniques is Signature verification as compared to traditional handcrafted system, where a forger has access and also attempt to imitate it which is used in commercial scenarios, like bank check payment, business organizations, educational institutions, government sectors, health care industry etc. so the signature verification process is used for human examination of a single known sample. There are mainly two types of signature verification: static and dynamic. i) Static or off-line verification is the process of verifying an electronic or document signature after it has been made, ii) Dynamic or on-line verification takes place as a person creates his/her signature on a digital tablet or a similar device. As compared, Offline signature verification is not efficient and slow for a large number of documents. Therefore although vast and extensive research on signature verification there is need to more focus and review on the online signature verification method to increase efficiency using deep learning.

A Secure Cryptography Based Clustering Mechanism for Improving the Data Transmission in MANET

Providing security to the Mobile Ad-hoc Network (MANET) is one of the demanding and critical tasks in recent days, due to its dynamic nature. For this reason, the various routing protocols and security mechanisms are developed for the traditional networks. Nonetheless, it still lacks the limitation of increased computational complexity, inefficient security, reduced throughput, and increased delay. To solve these problems, this paper developed a new system, namely, Secure Cryptography based Clustering Mechanism (SCCM) for MANET. It comprised the following stages: secure routing, encryption, signature generation, signature verification, and decryption. After forming the network, the connection between the mobile nodes was formed. After that, the secured routing was created between the source and destination by implementing the AOMDV routing protocol. Then, the original packet was converted into an unknown format by employing an Elliptic Curve Cryptography (ECC) encryption mechanism. Consequently, the signature for the encrypted packet was generated and forwarded to the destination via the Region Head (RH) and other gateway nodes. When the destination node received the packet, it performed the signature verification process for verifying whether the packet is valid or invalid. If it were valid, the receiver would accept the data and decrypt it by using the ECC decryption mechanism; otherwise, it would reject the packet and report to the base station. The simulation results evaluated the performance of the proposed security mechanism by using various measures and compared it with other techniques for proving the superiority.

Comment on Security and Improvement of Partial Blind Signature Scheme and Revocable Certificateless Signature Scheme

Partial blind signature scheme (BLD-SS) can protect the specific content of the signed message by adding factor to blind the signed message and can also prevent the misuse of the signature by adding common information negotiated in advance. Revocable certificateless signature scheme (RCSS) can prevent the user’s privacy from leaking by adding time key in the signature process and signature verification process. BLD-SS and RCSS both have wide applications in electronic-voting, electronic-cash system and other fields. In this paper, it presents a security analysis on a partial blind signature scheme and a revocable signature scheme proposed recently. It points out that the BLD-SS does not satisfy the unforgeability. A malicious user can forge a partial blind signature on any message and any common information without known from PKG (Private Key Generation). It also shows that the RCSS does not satisfy the unforgeability. A malicious enemy can forge a signature on any message and on any user. The PKG also cannot update the time key immediately when a user’s key is leaked or his identity is expired. In order to overcome these problems, it proposes some simple improvement methods which are almost the same efficiency with the original signature scheme.

Automatic Signature Verification in the Mobile Cloud Scenario: Survey and Way Ahead

On-line signature verification is typically carried out with the use of digitizing tablets specifically designed for the aim. So far, stand-alone systems have been mainly inspected, but the current distributed/cloud scenario and the amount of mobile devices in everyday life is calling for a new challenge. Within this scenario, signatures are acquired around the world with different kinds of devices and processed on multiple platforms in order to be verified. Through the paper, the different phases of the signature verification process in the new scenario are presented and the most valuable results are discussed considering the following aspects: accessibility and usability, interoperability, security and performance. Achievements as well as weakness are focused to highlight promising directions for further research and technology development.

A certificateless ring signature scheme with batch verification for applications in VANET

One of the most important challenges in secure and efficient communications for Vehicular Ad hoc NETworks (VANET) is proposing an appropriate authentication scheme. On the other hand, commonly used authentication approaches cannot be directly applied since they would lead to the violation of the privacy of the involved vehicles. In this paper, to address the security and privacy issues in VANETs, we propose an efficient certificateless ring signature (BV-CLRS) with batch verification that ensures conditional privacy preservation authentication for VANET communication. Significantly, the BV-CLRS reduces the computational and communication overhead. Moreover, our security analysis shows that the proposed scheme is more efficient than the existing schemes in case of the signature generation and verification process.

A Multi Recipient Aggregate Signcryption Scheme Based on Elliptic Curve

A cryptographic technique called signcryption combines the role of digital signature and encryption in a single logical step. This helps in reducing the computational cost associated with the traditional approach of signature-then-encryption technique. In last two decades, several signcryption schemes were proposed for single and multiple recipients, each having its own drawbacks and benefits. In this paper, we introduced an efficient signcryption scheme that uses the elliptic curve cryptography and is capable of transmitting data to multiple recipients. The scheme generates single signature by aggregating the multiple signatures for authentication and verification of data. This reduces the communication and computational cost associated with the signature generation and verification process. The analysis of the scheme shows that it has resistance against many known cryptographic attacks. Our scheme has the security features of non-repudiation, unforgeability, message confidentiality, forward secrecy, integrity, authentication and unforgeability.

An Offline Signature Verification and Forgery Detection Method Based on a Single Known Sample and an Explainable Deep Learning Approach

Signature verification is one of the biometric techniques frequently used for personal identification. In many commercial scenarios, such as bank check payment, the signature verification process is based on human examination of a single known sample. Although there is extensive research on automatic signature verification, yet few attempts have been made to perform the verification based on a single reference sample. In this paper, we propose an off-line handwritten signature verification method based on an explainable deep learning method (deep convolutional neural network, DCNN) and unique local feature extraction approach. We use the open-source dataset, Document Analysis and Recognition (ICDAR) 2011 SigComp, to train our system and verify a questioned signature as genuine or a forgery. All samples used in our testing process are collected from a new author whose signatures are not present in the training or other stages. From the experimental results, we get the accuracy between 94.37% and 99.96%, false rejection rate (FRR) between 5.88% and 0%, false acceptance rate (FAR) between 0.22% and 5.34% in our testing dataset.

Efficient Lattice-Based Ring Signature for Message Authentication in VANETs

In this article, we propose an efficient lattice-based ring signature scheme for message authentication in vehicular ad-hoc networks (VANETs). It provides unconditional identity privacy preservation, message authentication, and location privacy for the sender vehicle. The authentication of messages is extremely vital as it gives motivation for vehicles to accept, act, and further transmit messages to other vehicles in the network as well as remain a member of the network. Any unauthorized vehicle cannot transmit fraud messages into the network if messages are authenticated. The public key cryptography-based methods are more relevant for message authentication because of their easy key management and local derivation of keys. However, they add complexity to the whole network and require significant storage and computational power. Also, the predictable mobility characteristics of vehicles moving on the road make them vulnerable to tracking. This requires an additional mechanism to preserve location privacy. The proposed scheme provides unconditional identity privacy and location privacy. It is more efficient than the existing ring signature schemes in case of signature generation and verification process. Theoretical analysis and experimental results show that the proposed scheme provides security and unconditional privacy to vehicles in VANETs.

Handwritten signature verification using shallow convolutional neural network

Handwritten signatures are an undeniable and unique way to prove the identity of persons. Owing to the simplicity and uniqueness, it finds an essential place in the area of behavioral biometric. Signatures are the most widely accepted biometric trait by law enforcement agencies/personnel for verification purposes, especially in financial institutions, legal transactions, etc. and hence secured authentication becomes imperative. In the era of a digital age, numerous transactions are taking place, where handwritten signature verification is required by the agencies, e.g., banks, etc. In such scenarios, the process of signature verification, besides being accurate and secure, should be very fast, i.e., the real-time verification can be done. In this paper, we have proposed a convolutional neural network-based language-independent shallow architecture (sCNN(Shallow Convolutional Neural Network)) for signature verification. The proposed architecture is very simple but extremely efficient in terms of accuracy. A custom shallow convolution neural network is used to automatically learn the features of signature from the provided training data. Another contribution of the research work, which is the handwritten signature data collection for 137 subjects and 467 subjects, which are named as CVBLSig-V1 and CVBLSig-V2 respectively, has been reported in this paper. The performance of the proposed architecture has been evaluated on publicly available datasets, i.e., MCYT-75, MCYT-100, and GPDS, as well as CVBLSig-V1 and CVBLSig-V2. The performance was also compared with state of the art reported methods and shown improved, while considering the accuracy and equal error rate (EER) as performance metrics.

On Challenges in Verifying Trusted Executable Files in Memory Forensics

Memory forensics is a fundamental step in any security incident response process, especially in computer systems where malware may be present. The memory of the system is acquired and then analyzed, looking for facts about the security incident. To remain stealthy and undetected in computer systems, malware are abusing the code signing technology, which helps to establish trust in computer software. Intuitively, a memory forensic analyst can think of code signing as a preliminary step to prioritize the list of processes to analyze. However, a memory dump does not contain an exact copy of an executable file (the file as stored in disk) and thus code signing may be useless in this context. In this paper, we investigate the limitations that memory forensics imposes to the digital signature verification process of Windows PE signed files obtained from a memory dump. These limitations are data incompleteness, data changes caused by relocation, catalog-signed files, and executable file and process inconsistencies. We also discuss solutions to these limitations. Moreover, we have developed a Volatility plugin named sigcheck that recovers executable files from a memory dump and computes its digital signature (if feasible). We tested it on Windows 7 x86 and x64 memory dumps. Our experiments showed that the success rate is low, especially when the memory is acquired from a system that has been running for a long time.