Signature Verification Technique Research Articles

Enhancing Signature Forgery Detection System Using CNN-SVM

Handwritten signatures are widely used as a means of personal identification and authentication. Many documents like bank cheques and legal transactions require signature verification. But considering a large number of documents, it is a very difficult and time-consuming task. Therefore, ensuring the necessity for a robust automatic signature verification tool that aims to reduce fraud in all related financial transaction sectors. The current visual verification depends mainly on the experience, mood, and working environment of the verifier which ultimately wastes both time and money. Moreover, it is difficult for the eyes of any experts to precisely verify the ratios between lines and angles of a genuine signature to a fraud signature. Therefore, we propose an automatic signature verification technique using the recent advances in image processing and machine learning.

Handwritten Signature Verification System Using User-dependent Approach: A Comparative Study

Handwritten signature verification is a critical aspect of personal authentication in numerous sectors, including banking, legal, and access control systems. This paper provides a comprehensive investigation into writer-dependent signature verification techniques, spanning from traditional approaches such as Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) to cutting-edge deep learning architectures like Siamese and triplet networks. Through extensive experimentation and meticulous analysis conducted on benchmark datasets, we rigorously compare and evaluate the efficacy of these approaches. Our study reveals intriguing insights into the performance characteristics of different methodologies, shedding light on their strengths, weaknesses, and areas for improvement. Notably, we observe that while traditional feature-based methods excel in capturing discriminative information from signature images, deep learning architectures offer enhanced flexibility and adaptability, particularly in scenarios with large and diverse datasets. By combining the strengths of both paradigms, we demonstrate the potential for building robust and reliable signature verification systems capable of handling real-world challenges and variations. We highlight the importance of carefully curated datasets and well-designed network architectures in achieving optimal results, thereby providing valuable guidelines for practitioners and researchers in the field. In conclusion, this study contributes significant insights to the advancement of handwritten signature verification systems, emphasizing the importance of leveraging both traditional methodologies and modern deep learning techniques for building secure and dependable authentication mechanisms. By addressing key challenges and exploring novel approaches, our research aims to foster innovation and enhance the security measures associated with handwritten signature authentication in diverse applications. Keywords—Handwritten signature verification, Writer-dependent approach, Feature-based methods, Deep learning, Siamese networks, Triplet networks, Benchmark datasets, Performance evaluation, Authentication mechanisms, Security measures.

A Genetic Algorithm Based One Class Support Vector Machine Model for Arabic Skilled Forgery Signature Verification

Recently, signature verification systems have been widely adopted for verifying individuals based on their handwritten signatures, especially in forensic and commercial transactions. Generally, feature extraction and classification tremendously impact the accuracy of system authentication. Feature extraction is challenging for signature verification systems due to the diverse forms of signatures and sample circumstances. Current signature verification techniques demonstrate promising results in identifying genuine and forged signatures. However, the overall performance of skilled forgery detection remains rigid to deliver high contentment. Furthermore, most of the current signature verification techniques demand a large number of learning samples to increase verification accuracy. This is the primary disadvantage of using deep learning, as the figure of signature samples is mainly restricted to the functional application of the signature verification system. In addition, the system inputs are scanned signatures that comprise noisy pixels, a complicated background, blurriness, and contrast decay. The main challenge has been attaining a balance between noise and data loss, since some essential information is lost during preprocessing, probably influencing the subsequent stages of the system. This paper tackles the aforementioned issues by presenting four main steps: preprocessing, multifeature fusion, discriminant feature selection using a genetic algorithm based on one class support vector machine (OCSVM-GA), and a one-class learning strategy to address imbalanced signature data in the practical application of a signature verification system. The suggested method employs three databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Experimental results depict that the proposed approach outperforms current systems in terms of false acceptance rate (FAR), false rejection rate (FRR), and equal error rate (EER).

Dynamic Signature Verification Technique for the Online and Offline Representation of Electronic Signatures in Biometric Systems

Biometric systems input physical or personal human characteristics for identification, authentication, and security purposes. With the advancement in communication and intelligent security systems, biometrics are programmed to validate electronic signatures (E-signatures) for online and offline authentication. This article introduces a dynamic signature verification technique (DSVT) using mutual compliance (MC) between the security system and the biometric device. The security system is responsible for online and offline signature approval using personal inputs from humans. This personal verification is related to the stored online/offline signatures using certificates provided for authentication. The certificate-based authentication is valid within a session for online representation. Contrarily, this authentication is valid for persons under offline conditions. In this mode of segregation, application-level authentication verification is performed. A conventional tree classifier for dynamic signature verification is used for differentiating online and offline signatures. Moreover, the security metrics—such as signing bit, key, and size—are verified for both modes using classifier learning. For the segregated mode, the validation of the above is required to be unanimous to accelerate the dynamicity. The proposed technique’s performance is analyzed using the authentication success rate, verification failing ratio, verification time, and complexity.

Efficient Handwritten Signatures Identification using Machine Learning

Any agreement or contract between two or more parties requires at least one party to employ a signature as evidence of the other parties' identities and as a means of establishing the parties' intent. As a result, more people are curious about Signature Recognition than other biometric methods like fingerprint scanning. Utilizing both Fourier Descriptors and histogram of oriented gradients (HOG) features, this paper presents an efficient algorithms for signature recognition. The use of Local binary patterns (LBP) features in a signature verification technique has been proposed. Using morphological techniques, the signature is encapsulated within a curve that is both symmetrical and a good match. Measured by the frequency with which incorrect patterns are confirmed by a given system, false acceptance rate (FAR) provides an indication of the effectiveness and precision of the proposed system. Using a local dataset of 60 test signature patterns, this investigation found that 10% were incorrectly accepted for FAR of 0.169. Experiments are conducted on signature photos from a local dataset. Verification of signatures has previously made use of KNN classifier. KNN classifier produced higher FARs and recognition accuracies than prior techniques.

Improved Recurrent Neural Network Schema for Validating Digital Signatures in VANET

Vehicular ad hoc networks (VANETs) allow communication between stationary or moving vehicles with the assistance of wireless technology. Among various existing issues in smart VANETs, secure communication is the key challenge in VANETs with a 5G network. Smart vehicles must communicate with a broad range of advanced road systems including traffic control and smart payment systems. Many security mechanisms are used in VANETs to ensure safe transmission; one such mechanism is cryptographic digital signatures based on public key infrastructure (PKI). In this mechanism, secret private keys are used for digital signatures to validate the identity of the message along with the sender. However, the validation of the digital signatures in fast-moving vehicles is extremely difficult. Based on an improved perceptron model of an artificial neural network (ANN), this paper proposes an efficient technique for digital signature verification. Still, manual signatures are extensively used for authentication across the world. However, manual signatures are still not employed for security in automotive and mobile networks. The process of converting manual signatures to pseudo-digital-signatures was simulated using the improved Elman backpropagation (I-EBP) model. A digital signature was employed during network connection to authenticate the legitimacy of the sender’s communications. Because it contained information about the vehicle on the road, there was scope for improvement in protecting the data from attackers. Compared to existing schemes, the proposed technique achieved significant gains in computational overhead, aggregate verification delay, and aggregate signature size.

Recognition of Handwritten Medical Prescription Using Signature Verification Techniques.

Patient record keeping plays a vital role in diagnoses and cures. Due to a shortage of time, most doctors write prescriptions manually in Pakistan. At times, it becomes difficult for pharmacists to read prescriptions properly. As a result, they may dispense the wrong medicine. This might cause risky and deadly effects on the patient's health. This paper proposes an online handwritten medical prescription recognition system that lets doctors write prescriptions on a tablet using a stylus and automatically recognizes the medicine. We use signature verification techniques to recognize the doctor's handwriting to overcome the problem of misinterpretation of the medicine name by the pharmacist. The proposed system stores different features like the pen coordinates, time, and several pen-ups and pen-downs. Besides using features already proposed in the literature for signature verification, we propose some new features that greatly enhance recognition accuracy. We built a dataset of 24 medicine names from two users and compared results using newly proposed features. We have obtained 84%, 78%, 77.47% 77.31%, 74.17%, 60%, 38.5%, 68%, and 61.64% accuracies for 9 users using SVM classifier.

Smartphone-based Handwritten Signature Verification using Acoustic Signals

Handwritten signature verification techniques, which can facilitate user authentication and enable secure information exchange, are still important in property safety. However, on-line automatic handwritten signature verification usually requires dynamic handwritten patterns captured by a special device, such as a sensor-instrumented pen, a tablet or a smartwatch on the dominant hand. This paper presents SonarSign, an on-line handwritten signature verification system based on inaudible acoustic signals. The key insight is to use acoustic signals to capture the dynamic handwritten signature patterns for verification. Particularly, SonarSign exploits the built-in speakers and microphones of smartphones to transmit a specially designed training sequence and record the corresponding echo for channel impulse response (CIR) estimation, respectively. Based on the sensitivity of CIR to the tiny surrounding environment changes including handwritten signature actions, SonarSign designs an attentional multi-modal Siamese network for end-to-end signatures verification. First, multi-modal CIR streams are fused to extract representative signature pattern features from spatio-temporal dimensions. Then an attentional Siamese network is elaborated to verify whether the given two signatures are from the same signatory. Extensive experiments in real-world scenarios show that SonarSign can achieve accurate and robust signatures verification with an AUC (Area Under ROC (Receiver Operating Characteristic) Curve) of 98.02% and an EER (Equal Error Rate) of 5.79% for unseen users.

Signature identification and verification techniques: state-of-the-art work

Signature identification and verification techniques: state-of-the-art work

Elliptic Curve Digital Signature technique based Abnormal Node Detection in Wireless AD HOC Networks

This Security, as well as routing Performance, is two significant operations of Wireless ad-hoc networks (WANETs). Even though, several efforts to enhance its secure routing scheme of WANETs continue susceptible to attacks. Contrasting the majority of conventional solutions which avoid the precise problems, this scheme inclines to distinguish the misconduct as well as recognize the abnormal nodes in WANETs. In this paper, the Elliptic Curve Digital Signature technique (ECDST) is used to detect the abnormal node in the WANET. This technique function recognizes the abnormal node as well as isolate that node them from the route. During route discovery, the ECC method utilizing the Weierstrass Elliptic functions for detecting the abnormal node. The abnormal nodes are discovered by signature verification technique on data communication. It attains consistency in routing with disqualifying the link by the utmost weight as well as validates the nodes. This scheme assures node availability, integrity, as well as improves network performance. The observational analyses illustrate that the introduced scheme can notice the abnormal as well as a compromised node in the network.

OSVFuseNet: Online Signature Verification by feature fusion and depth-wise separable convolution based deep learning

Online Signature Verification (OSV) techniques have been deployed in production systems for decades, yet training the model for efficient classification of the test signature from fewer training signature samples is still an open challenge. The advancements in Convolutional Neural Networks (CNNs) enormously boosted the effectiveness of OSV systems. However, learning subtle and discriminating representations from few training samples to classify the genuineness of test signature has not been explored fully. In this paper, a Convolution Autoencoder (CAE) is used to obtain high-level feature representations from the input signature and these high level features are fused with handcrafted features to constitute a hybrid feature set. The hybrid set of features is presented as an input to an Online Signature Verification framework made up of Depth-wise Separable Convolutional Neural Network (DWSCNN). DWSCNN effectively learn deep feature representations with fewer training samples and parameters than traditional CNNs resulting in a light weight OSV framework. Thorough experimental analysis on three benchmark datasets MCYT-100 (DB1), SVC-2004-Task2 and SUSIG-Visual corpus confirm that the proposed hybrid fusion of feature set and DWSCNN based OSV framework achieve higher classification accuracies and outperforms many contemporary and state-of-the art OSV models.

Signature Verification and Bloom Hashing Technique for Efficient Cloud Data Storage

Cloud computing is a one of the network-based computing that provides services to computer and other cloud devices. Secure cloud data storage is a significant when using the cloud services provided by the service provider in cloud environment. Many research works have been developed to provide secure wireless communication in cloud. However, integrity verification of stored data on cloud was not addressed in an efficient manner. In order to overcome above limitations, bloom hashing based Schnorr signature (BH-SS) technique is proposed. In BH-SS technique, at first, the cloud users send the request to cloud server and it response the user requested data. Next, bloom hash function is used to increase the integrity of cloud data storage. Bloom hash function is an efficient data structure used for storing the data and its hashed value. Bloom hash function stores the various cloud user data in it bits of array by generating hash value for each cloud data. Each data has the diverse hash value in bit array. This helps for BH-SS technique to improve the data integrity and dynamic data storage in cloud environment. Lastly, BH-SS technique employed Schnorr signature to ensure confidentiality of stored data with the aid of producing private signing key and public verification key. Therefore, proposed BH-SS technique improves the data integrity rate and reduces execution time. The BH-SS technique conducts experimental works on the parameters such as data integrity, space complexity, execution time and data confidentiality rate. The experimental results observe that the BH-SS technique is able to enhance the cloud data integrity and also reduces the execution time of cloud storage and space complexity when compared to state-of-the-art-works.

Online handwritten signature verification using feature weighting algorithm relief

Online handwritten signatures are widely used as a reliable identity authentication technology in industries such as banking, insurance and hospitals. Most existing online handwritten signature verification schemes usually choose the same feature sets for different users while comparing different signatures, which may reduce the stability of the dynamic characteristics. To avoid this problem, we propose a novel writer-dependent online signature verification technique based on Relief. For each user, our verification scheme falls into two phases: the training phase and the test phase. In the training phase, we select a signature as the base signature from real signatures and construct two different kinds of signature pairs: the real signature pairs and the forged signature pairs. Each pair of real signatures consists of two real signatures, and each pair of forgeries is made up of a real signature and a forgery. A group of combined features are defined for each pair of signatures. Then, we select more stable combined features by means of the Relief algorithm and compute a matrix used as a matching template, which contains the value of each combined feature for each pair of signatures. In the test phase, the classification of the pair of signatures composed of the test signature and the base reference signature is determined by the K-nearest neighbor. Experiments are conducted on our own database and SVC2004, and the results indicate that the method we propose here shows lower FAR and FRR than do traditional methods on the same databases.

Review on Offline Signature Recognition and Verification Techniques

Review on Offline Signature Recognition and Verification Techniques

Features selection for offline handwritten signature verification: state of the art

This research comes out with an in-depth review of widely used techniques to handwritten signature verification based, feature selection techniques. The focus of this research is to explore best features selection criteria for signature verification to avoid forgery. This paper further present pros and cons of local and global features selection techniques, reported in the state of art. Experiments are conducted on benchmark databases for signature verification systems (GPDS). Results are tested using two standard protocols; GPDS and the program for rate estimation and feature selection. The current precision of the signature verification techniques reported in state of art are compared on benchmark database and possible solutions are suggested to improve the accuracy. As the equal error rate is an important factor for evaluating the signature verification's accuracy, the results show that the feature selection methods have successfully contributed toward efficient signature verification.

Adaptive Security for Intelligent Transport System Applications

The transportation system is gradually migrating toward autonomous, electric and intelligent vehicles. Wireless-enabled vehicles along with infrastructure units on the road are connected with traffic management centers that use intelligent data analysis tools to efficiently manage city's traffic. However, such wireless connectivity can make the ITS networks vulnerable to security threats; thus, impacting the application's reliability. On the other hand, the use of robust security techniques could hamper applications' quality of service (QoS). To understand the interplay between these two conflicting requirements, this article reviews the security and QoS design challenges in the ITS aspect of smart cities. Using an experimental test-bed, we evaluate the standard compliant security processing delays, develop an on-line tool that presents detailed security benchmark results, and study the impact of security on QoS using simulation results. We also discuss how machine learning based adaptive signature verification techniques can enhance QoS in ITS. We further present future opportunities to optimize the security-QoS balance for ITS applications.

A Novel Online Signature Verification System Based on GMM Features in a DTW Framework

This paper presents a novel online signature verification system based on the extension of the traditional dynamic time warping (DTW) matching scheme. We propose the use of a set of features derived from a Gaussian mixture model (GMM) for the alignment of the signatures using DTW. These features aid in capturing signature-dependent characteristics of a user in the feature space with a probabilistic framework. In addition, we explore the characteristics of the warping path of DTW, by employing the proposed GMM features. We derive a score for the warping path, and fuse it to that of the DTW score for verifying the authenticity of a test signature. To the best of our knowledge, this paper is the first of its kind that uses the features of the GMM, a model-based classifier into the framework of the DTW technique for online signature verification. The experiments are conducted on the publicly available MCYT database for both common and user thresholds. The results obtained are promising over prior works for this database.

AUTOMATIC SIGNATURE VERIFICATION WITH CHAIN CODE USING WEIGHTED DISTANCE AND EUCLIDEAN DISTANCE - A REVIEW

The signature forgery can be restricted by either online or offline signature verification techniques. It verifies the signature by performing a match with the pre-processed signature dynamically by detecting the motion of stylus during signature while on other hand, offline verifies by performing a match using the two dimensional scanned image of the signature. This paper studies about the various techniques available in offline signature verification along with their shadows.

Image Processing Based Signature Verification Technique to Reduce Fraud in Financial Institutions

Handwritten signature is broadly utilized as personal verification in financial institutions ensures the necessity for a robust automatic signature verification tool. This tool aims to reduce fraud in all related financial transactions’ sectors. This paper proposes an online, robust, and automatic signature verification technique using the recent advances in image processing and machine learning. Once the image of a handwritten signature for a customer is captured, several pre-processing steps are performed on it including filtration and detection of the signature edges. Afterwards, a feature extraction process is applied on the image to extract Speeded up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT) features. Finally, a verification process is developed and applied to compare the extracted image features with those stored in the database for the specified customer. Results indicate high accuracy, simplicity, and rapidity of the developed technique, which are the main criteria to judge a signature verification tool in banking and other financial institutions.

The Analysis of Online and Offline Signature Verification Techniques to Counter Forgery

Background: This paper reports precise examination of a few highlight extraction-based procedures for signature check. Dynamic features of Signature are captured for online verification procedure at the time of signing. Offline frameworks deal with the examined picture of a signature. A shot at the Offline Verification of signatures utilizing an arrangement of shape based geometric highlights has been taken. Pattern Slant Angle, Normalized Area, Height, Aspect Ratio, Width, area of signature pixels are the utilized features. Captured image should be Pre-processed to separate the signature part and remove the white noise, before extracting the features. Methods: The framework is at first prepared utilizing a database of signatures acquired from those people whose signatures must be confirmed by the framework. A mean signature is acquired coordinating the above features got from an authenticate test signatures. Findings: The claimed test signature is compared with the mean signature for the purpose of verification. Euclidian distance in the feature space between the claimed signature and the template serves as a measure of similarity between the two. On the off chance that this separation is not as much as a predefined edge (relating to least adequate level of likeness), the test signature is checked to be that of the guaranteed subject else distinguished as a phony.