Genuine Signatures Research Articles

The Death of God as a Turn to Radical Theology: Then and Now

This article begins with a short reconstruction of the long-forgotten Death of God movement and its development of the concept of a Radical Theology during the 1960s in the United States of America and in Western Germany (The “Death of God” as a Signature of Secular Culture). In my view, protestant theology and the church have thus far failed to discuss Nietzsche’s proclamation of the “Death of God” as a genuine signature of modernity in a constructive way. In refusing the debate, they did not see the theological potential of the diagnostic and ambiguous metaphor “Death of God” as a “tremendous event still on its way”. With regard to a current perspective on Radical Theology (The Rise of Religious Fundamentalism and the Renaissance of Radical Theology), the intention is a creative and radical interpretation of this heretical concept as a step towards a modern religiosity after (the Death of) God which includes a new awareness of the finitude of human life and of its limitations. The theological symbol of the “Death of God” in my concept of “Heretical Religiosity” leads to a new approach to the theology of wisdom as symbolized in the Hebrew Bible by the book of Ecclesiastes (Heretical Religiosity: Radical Theology and Wisdom Literature in the Hebrew Bible).

Verification of the person’s dynamic signature on a limited number of samples

Objectives. The goal of the research is to develop a new person-dependent method for verification of a signature of one person made on a tablet with a stylus in the presence of a limited number of signature samples of this person.Methods. The paper shows how to construct an individual pattern of the dynamic signatures of any person, which is described by points in a multidimensional feature space and is intended for subsequent verification of the authenticity of the signatures of a given person. It is constructed using 5<N<20 samples of genuine human signatures. The pattern forms a convex object in a multidimensional feature space. It describes the peculiar properties of a signature performed by a specific person.Results . The dynamics of signature execution is represented by three discrete parametric functions: coordinates of the stylus X, Y and its pressure on the tablet P, recorded at fixed time intervals. In the process of research, a number of secondary functions-features were selected and calculated from them. Since these data sets have different lengths, the dynamic time warping algorithm is used to compare them. The results of this transformation are distances between the dynamic features of two signatures, which serve as coordinates of a point in the feature space that describes the similarity of these signatures. The set of such points describes similarity of all pairs of genuine human signatures presented for verification in a multidimensional feature space. The convex hull of the cloud of these points is used as a pattern of a particular person's signature. The genuine signatures of any person are always different from each other; significant differences between them can distort the verification result.Conclusion. Experimental studies performed on genuine and fake signatures of 498 people from the largest available database of dynamic signatures, DeepSignDB, showed a verification accuracy of about 98 % when analyzing 24,900 signatures. Half of them are genuine, half are fake

A Novel Method of Fake Signature Detection Using Deep Learning Techniques

In today's digital world, making sure signatures are real is super important. Sometimes, though, people fake signatures, causing problems for money and legal stuff. This research looks into fixing this issue by finding a new way to catch fake signatures. We know that nowadays, a lot of things happen online, like signing documents and doing money stuff, and we need to make sure signatures are real. This paper introduces a fresh perspective by leveraging Convolutional Neural Networks (CNN), a deep learning technique. The objective is to enable the CNN model to autonomously learn and distinguish genuine signatures from counterfeits, offering a dynamic and adaptive solution to the evolving landscape of forgery. The research encompasses the development and application of a novel signature detection model, highlighting its adaptability and effectiveness. Through experimentation with diverse datasets and the optimization of hyperparameters, the approach aims to significantly enhance the accuracy of fake signature detection. The outcome of this research holds the promise of bolstering the security of digital transactions and contributing to the evolution of more robust signature verification systems. Keywords: Forged Signature, Convolutional Neural Networks (CNN), Digital Transactions, Signature Authentication

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.

Exploring Lee-Yang and Fisher zeros in the 2D Ising model through multipoint Padé approximants

We present a numerical calculation of the Lee-Yang and Fisher zeros of the 2D Ising model using multipoint Padé approximants. We perform simulations for the 2D Ising model with ferromagnetic couplings both in the absence and in the presence of a magnetic field using a cluster spin-flip algorithm. We show that it is possible to extract genuine signature of Lee-Yang and Fisher zeros of the theory through the poles of magnetization and specific heat, using the multipoint Padé method. We extract the poles of magnetization using Padé approximants and compare their scaling with known results. We verify the circle theorem associated to the well known behavior of Lee-Yang zeros. We present our finite volume scaling analysis of the zeros done at T=Tc for a few lattice sizes, extracting to a good precision the (combination of) critical exponents βδ. The computation at the critical temperature is performed after the latter has been determined via the study of Fisher zeros, thus extracting both βc and the critical exponent ν. Results already exist for extracting the critical exponents for the Ising model in two and three dimensions making use of Fisher and Lee-Yang zeros. In this work, multipoint Padé is shown to be competitive with this respect and thus a powerful tool to study phase transitions. Published by the American Physical Society 2024

Handwritten online signature verification and Forgery Detection for Online Handwritten Signature using Hybrid Wavelet Transform-1 with HMM classifier

Online signature verification employs a distinctive biometric trait by utilizing both static and dynamic features extracted from 2D signature images. A hybrid wavelet transform, denoted as HWT-1 with a size of 256, is formed through the Kronecker product of two orthogonal transforms, such as DCT, DHT, Haar, Hadamard, and Kekre, each with sizes 4 and 64. This HWT facilitates signal analysis at both global and local levels, akin to traditional wavelet transforms. Specifically, HWT-1 processes the 256 samples of online handwritten signatures, yielding 128 samples that constitute the feature vectors for signature verification and forgery detection. These feature vectors are then inputted into the Left-Right and Ergodic Hidden Markov Model (HMM) classifiers for further analysis. The HMMs are trained using 10 randomly selected genuine signature samples and subsequently tested with the remaining 10 genuine signatures and 20 forged signatures from the SVC 2004 signature database, repeating this process 20 times to compute average values. Among all possible combinations of HWT-1 utilizing DCT, DHT, Haar, Hadamard, and Kekre transforms for the Left-Right HMM model, the combination of DCT 4 and Haar 64 demonstrates the best performance with a False Rejection Rate (FRR) and False Acceptance Rate (FAR) of 1.05% and 0.99%, respectively, at state 3. Similarly, considering all feasible combinations of HWT-1 for the Ergodic HMM model, the combination of DCT 4 and Kekre 64 yields the optimal performance with an FRR and FAR of 1.24% and 1.33%, respectively, at state 3.

Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-2 with HMM Classifier

Online signature verification stands out as a distinctive biometric feature, offering both static and dynamic attributes within 2D signature images. A Hybrid Wavelet Transform-2 (HWT-2) with a size of 256 is constructed by employing the Kronecker product of two orthogonal transforms: DCT, DHT, Haar, Hadamard, and Kekre, each with sizes of 4 and 64. The HWT enables the analysis of signals at both global and local levels, akin to wavelet transforms. HWT-2 is applied to 256 samples of online handwritten signatures, and the first 128 samples of the output are utilized as feature vectors for the verification and forgery detection of online handwritten signatures. These feature vectors are inputted into Left-Right and Ergodic Hidden Markov Model (HMM) classifiers for analysis. The HMMs are trained using 10 randomly selected genuine signature samples, and subsequently tested on the remaining 10 genuine signatures and 20 forged signatures from 40 users of the SVC 2004 signature database. This process is repeated 20 times, and the average values are computed. Among all possible combinations of HWT-2 using DCT, DHT, Haar, Hadamard, and Kekre transforms for the Left-Right HMM model, the combination of DCT 4 and DHT 64 demonstrates the best performance, with False Rejection Rate (FRR) and False Acceptance Rate (FAR) values of 3.96% and 1.48%, respectively, for state 5. Similarly, for the Ergodic HMM model, the combination of DCT 4 and DHT 64 exhibits the best performance, with FRR and FAR values of 1.10% and 2.88%, respectively, for state 5. These results indicate that combinations of HWT-2 outperform individual orthogonal transforms, and further, that HWT-2 combinations within the Ergodic HMM model offer superior performance compared to the Left-Right HMM model.

Comparative Analysis of Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-1 and 2 with HMM Classifier

Online signature verification is a unique biometric feature. Provides static and dynamic features for 2D signature images. Hybrid wavelet transform -1 and 2 (HWT-1 and HWT-2) of size 256 is created using the Kronecker product of two orthogonal transforms such as DCT, DHT, Haar, Hadamard and Kekre with size 4 and 64. HWT has the ability to analyze signals such as wavelet transform at global and local levels. HWT-1 and HWT-2 are used for the 256 samples of the online Handwritten signature and the first 128 samples of the output are used as feature vectors for handwritten online signature verification and forgery detection. This feature vector is given to the Left-Right and Ergodic modelled HMM classifier for analysis. HMM is trained using 10 randomly chosen genuine signature samples and is used to test remaining 10 genuine signatures and 20 forged signatures of 40 users of SVC 2004 signature database. This process is iterated 20 times and then average values are calculated. Considering all the possible combination of HWT-1 and HWT-2 for DCT, DHT, Haar, Hadamard and Kekre transform for Left Right HMM model, DCT 4 Haar 64 HWT-1 offers best performance of FRR, FAR of 1.05%, 0.99% respectively for state 3. Considering all the possible combination of HWT-1 and HWT-2 for DCT, DHT, Haar, Hadamard and Kekre transform for Ergodic HMM model, DCT 4 DHT 64 offers best performance of FRR, FAR of 1.10%, 2.88% respectively for state 5. We conclude that comparing HWT-1 and HWT-2 combinations for Left Right and Ergodic HMM model, HWT-1 for Left Right model offer better performance.

Application of nonparametric quantifiers for online handwritten signature verification: A statistical learning approach

AbstractThis work explores the use of nonparametric quantifiers in the signature verification problem of handwritten signatures. We used the MCYT‐100 (MCYT Fingerprint subcorpus) database, widely used in signature verification problems. The discrete‐time sequence positions in the x ‐axis and y‐axis provided in the database are preprocessed, and time causal information based on nonparametric quantifiers such as entropy, complexity, Fisher information, and trend are employed. The study also proposes to evaluate these quantifiers with the time series obtained, applying the first and second derivatives of each sequence position to evaluate the dynamic behavior by looking at their velocity and acceleration regimes, respectively. The signatures in the MCYT‐100 database are classified via Logistic Regression, Support Vector Machines (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). The quantifiers were used as input features to train the classifiers. To assess the ability and impact of nonparametric quantifiers to distinguish forgery and genuine signatures, we used variable selection criteria, such as: information gain, analysis of variance, and variance inflation factor. The performance of classifiers was evaluated by measures of classification error such as specificity and area under the curve. The results show that the SVM and XGBoost classifiers present the best performance.

Te Vacancy‐Driven Anomalous Transport in ZrTe5 and HfTe5

AbstractThe strongly sample‐dependent anomalous transport properties observed in the layered Dirac materials ZrTe5 and HfTe5 are known to strongly correlate with the presence of Te vacancies. One phenomenon, a negative longitudinal magnetoresistance (NLMR), is widely speculated to be a signature of broken chiral symmetry. However, the role of electronic structure in the sample dependence of the transport properties of these materials is poorly understood. This prompts the question as to whether the NLMR is a genuine signature of the chiral anomaly in ZrTe5 and HfTe5. In this work, the effect of Te vacancies on the electronic structure of ZrTe5 and HfTe5 is investigated via first‐principles calculations. Te vacancies serve two purposes: modification of the cell volume via effective compressive strain and production of local changes to the electronic structure. The reorganization of the electronic structure near the Fermi energy indicates that Te vacancies can rationalize conflicting reports in spectroscopic and transport measurements that have remained elusive in prior first‐principles studies. These results show that Te vacancies contribute, in part, to the anomalous transport properties of ZrTe5 and HfTe5 but, critically, do not eliminate the possibility of a genuine manifestation of the chiral anomaly in these materials.

Analysis of Signature Patterns: Consistency and Distinctiveness in Handwritten Signatures for Forensic Authentication

Handwritten signatures are unique behavioural attributes that serve as a distinct form of identification forindividuals. This study focuses on the analysis of signature patterns using various parameters, including aspect ratio, angle of the first letter with respect to the baseline, and the ratio of the area of a circle to its radius within the signature. A dataset consisting of 1200 genuine and 1200 simulated (English) signature samples from 12 individuals were examined using an image analysis tool to record measurements for these parameters. The primary objectives were to investigate the consistency of signatures within the same person and to compare genuine signatures with simulated ones. This study contributes to the field of forensic investigations by offering insights into the authentication of signatures and the measurement of dissimilarities between signatures of the same and different individuals based on their patterns. The findings aid in identifying most influential parameter in distinguishing genuine signatures from counterfeits and enhance the evidential value of handwritten signature samples. Additionally, the simplified approach employed in this study allows for universal applicability across languages. Future research endeavours could expand the dataset and explore additional parameters to further improve the accuracy and reliability of signature verification methods in forensic analysis.

Signature Verification System Using SSIM In Image Processing

The verification of signatures is an essential function in several domains, including financial, legal, and administrative processes. Thanks to advancements in image processing, automatic signature verification methods have become more popular. Using structural similarities and image analysis, the proposed research offers a novel approach to signature verification. To compare and assess signatures, it uses the SSIM index. The procedure begins with pre-processing the signature pictures to improve their quality and eliminate any artifacts or noise that may have been obtained from Adobe's stock library. Then, the structural similarity between the reference signature and the input signature is calculated. The perceptual resemblance of two images is measured using structure, contrast, and brightness. The goal of the proposed research is to use this measure to record the signature's structural features and spot changes or deviations. The SSIM value that comes out of the comparison is checked against a threshold that has already been set. To validate an input signature, the calculated similarity must be greater than a certain threshold. The document is marked as suspicious or possibly falsified if it does not comply. Experimental results have shown that the method is effective in differentiating between authentic and counterfeit signatures. By doing away with the need for subjective human judgment and physical examination, this technology provides a reliable and unbiased way to authenticate signatures. Increased automation and trust in signature authentication systems are possible because to the proposed method's encouraging results in accurately differentiating genuine signatures from fakes.

Metric Deviations between Genuine and Forged Signatures: A Mathematical Approach

Background: In the present study, a metric approach has been made to distinguish between genuine and forged signatures. Certain fixed points were selected and the range of variability between 14 different parameters was calculated. Our study provides mathematics-based corroborative evidence to support the opinion of Forensic Document Examiners. Methodology: For sample collection the samples were obtained in two different sets, in set 1 subject was guided to provide their genuine signature in a continuous running hand fashion and, in set 2, two individuals consisting of a male and a female were asked to forge the sample first by un-practiced hand and then later by the practiced hand i.e. after having practiced and then forge the sample, this exercise was also performed by 50 subjects. Therefore, 500 genuine signatures of 50 subjects were taken, each subject providing 10 samples of their original writing to understand the natural variation, master pattern and range of variability and 200 samples by forging the signature; 100 samples by male subjects and 100 samples by females; inclusive of both practiced and unpractised samples and allotted them a manually calculated mathematical value. This exercise was done for every sample. All the samples belonged to the age range of 20-25 years. Results: The study developed a scientific and reliable method to compare the practiced signature of the individual using a statistical metric deviation-based method. Conclusion: The conclusion showed that forged signatures fail to lie within the limits of the variability of the mathematical parameters taken.

Online Handwritten Signature Verification Method Based on Uni-Feature Correlation Coefficient between Signatures

Online handwritten signature verification is a crucial direction of research in the field of biometric recognition. Recently, many studies concerning online signature verification have attempted to improve performance using multi-feature fusion. However, few studies have provided the rationale for selecting a certain uni-feature to be fused, and few studies have investigated the contributions of a certain uni-feature in the multi-feature fusion process. This lack of research makes it challenging for future researchers in related fields to gain inspiration. Therefore, we use the uni-feature as the research object. In this paper, the uni-feature is one of the X and Y coordinates of the signature trajectory point, pen pressure, pen tilt, and pen azimuth feature. Aiming to solve the unequal length of feature vectors and the low accuracy of signature verification when using uni-features, we innovatively introduced the idea of correlation analysis and proposed a dynamic signature verification method based on the correlation coefficient of uni-features. Firstly, an alignment method of two feature vector lengths was proposed. Secondly, the correlation coefficient calculation formula was determined by analyzing the distribution type of the feature data, and then the correlation coefficient of the same uni-feature between the genuine signatures or between the genuine and forged signatures was calculated. Finally, the signature was verified by introducing a Gaussian density function model and combining it with the signature verification discrimination threshold. Experimental results showed that the proposed method could improve the performance of dynamic signature verification based on uni-features. In addition, the pen pressure feature had the best signature verification performance, with the highest signature verification accuracy of 93.46% on the SVC 2004 dataset.

Distribution of local curvature values as a structural feature for off-line handwritten signature verification

In the paper, a new feature for describing a digital image of a handwritten signature based on the frequency distribution of the values of the local curvature of the signature contours, is proposed. The calculation of this feature on the binary image of a signature is described in detail. A normalized histogram of distributions of local curvature values for 40 bins is formed. The frequency values recorded as a 40-dimensional vector are called the local curvature code of the signature.During verification, the proximity of signature pairs is determined by correlation between curvature codes and LBP codes described by the authors in [23]. To perform the signature verification procedure, a two-dimensional feature space is constructed containing images of the proximity of signature pairs. When verifying a signature with N authentic signatures of the same person, N(N-1)/2 patterns of the proximity of pairs of genuine signatures and N images of pairs of proximity of the analyzed signature with genuine signatures are presented in the feature space. The Support Vector Machine (SVM) is used as a classifier.Experimental studies were carried out on digitized images of genuine and fake signatures from two databases. The accuracy of automatic verification of signatures on the publicly available CEDAR database was 99,77 % and on TUIT was 88,62 %.

Dynamic feature analysis of handwritten electronic signatures based on Fourier transform.

The utilization of handwritten electronic signatures has expanded in various application scenarios, leading to an increased demand for identification. Unlike handwriting signatures, handwritten electronic signatures offer the advantage of extracting dynamic feature data, including writing pressure, velocity, and acceleration. In this study, the Fourier transform was employed to extract 18 characteristics from the time domain and frequency domain of writing pressure, velocity, and acceleration. The experimental findings revealed distinguishable differences between genuine signatures and random forgeries in writing pressure. However, no statistically significant differences were observed in writing velocity and writing acceleration. Moreover, significant differences were detected in most characteristics when comparing genuine signatures with freehand imitation forgeries and tracing imitation forgeries. The canonical discriminant analysis was performed between the genuine and Non-genuine signatures; the cross-validation estimated the discriminating powerof these characteristics with a satisfactory result. The study proposed a new approach to analyzing handwritten electronic signatures using time-domain and frequency-domain characteristics and demonstrated its effectiveness in the examination.

TransOSV: Offline Signature Verification with Transformers

Signature verification is a frequently-used forensics technology in numerous safety-critical situations. Although convolutional neural networks (CNNs) have made significant advancements in the field of signature verification, their reliance on local neighborhood operations poses limitations in capturing the global contextual relationships among signature strokes. To overcome this weakness, in this paper, we propose a novel holistic-part unified model named TransOSV based on the vision transformer framework to solve offline signature verification problem. The signature images are first encoded into patch sequences by the proposed transformer-based holistic encoder to learn the global signature representation. Second, considering the subtle local difference between the genuine signature and forged signature, we design a contrast based part decoder along with a sparsity loss, which are utilized to learn the discriminative part features. With the learned holistic features and part features, the proposed model is optimized by the contrast loss function. To reduce the influence of sample imbalance, we also formulate a new focal contrast loss function. Furthermore, we conduct the proposed model to learn signature representations for writer-dependent signature verification task. The experimental results demonstrate the potential of the proposed TransOSV model for both writer-independent and writer-dependent signature verification tasks, achieving remarkable performance improvements and competitive results on four widely-used offline signature datasets.

FC-ResNet: A Multilingual Handwritten Signature Verification Model Using an Improved ResNet with CBAM

Offline signature verification is a widely used biometric method in finance, law, and administrative procedures. However, existing deep convolutional neural network models perform poorly on signature datasets that span different regions and ethnic people, while also suffering from problems such as large parameter counts and slow inference speeds. To address these issues, we propose an improved residual network model (FC-ResNet). This model introduces a convolutional block attention module into the classical residual network to adapt to the diversity and variability of signatures, while also compressing the model for lightweight deployment. Due to the lack of public, offline handwritten signature datasets for ethnic people, we collected a large-scale offline handwritten signature dataset, including genuine signatures and forged signatures in Chinese, Uyghur, Kazakh, and Kirgiz, totaling 38,400 images. Our FC-ResNet model achieved an accuracy of over 96% for each language in our self-built dataset, as well as accuracy rates of 96.21%, 98.42%, and 97.28% on the public datasets CEDAR, BHSig-B, and BHSig-H, respectively. Based on the above experimental results, our proposed model demonstrates great potential for both public and self-built signature datasets, while also exhibiting significant advantages in lightweight model deployment. We believe that this work can provide a feasible solution for ethnic people signature verification.

Enhancing Signature Verification through Neural Network Ensemble

This study presents a signature authentication mechanism to prevent forgery. In the actual world, handling a large collection of data and detecting genuine signatures with reasonable accuracy is often difficult for any verification system. As a result, artificial intelligence techniques are used that can learn from a large data set during the training phase and reply effectively during the application phase without wasting a lot of storage memory space or processing time. It should also be able to refresh its expertise based on real-world encounters on a regular basis. A Multi-Layered Neural Network Model is one such adaptive machine learning technique that is used in this study. Initially, a massive amount of data is gathered by photographing several authentic and fake signatures. The image quality is increased by applying image processing, which is followed by the feature extraction phase, which extracts specific unique standard statistical features.

External effects on the characteristics of handwriting analysis and the analysis of forgery types.

This project studies the effects of time and hand dominance on different characteristics of handwriting analysis. The handwriting characteristics that were tested are line quality, slope of writing, pen lifts, letter spacing, and word spacing. These characteristics were tested in a subject’s dominant and non-dominant handwriting. Twenty-five adult human subjects participated in this experiment. The research was split into two parts. Part one analyzed the characteristics of a subject’s dominant and non-handwriting during the morning, afternoon, and evening of (preferably) the same day. In the part one phase, participants were asked to write: "The quick brown fox jumps over the lazy dog." Part two analyzed the authenticity of a freehand, simulation, and tracing forgery. A ruler was used to quantify the handwriting characteristics. The line quality of the dominant and non-dominant hands does not change during the different sessions. The slope of the writing changes more with the non-dominant hand. The non-dominant hand has greater letter and word spacing. The dominant hand typically changed the pen lifts number more than the non-dominant hand. “Quick” was the word that had the most changes in pen lifts for both the dominant and non-dominant hand. The freehand forgery is easy to classify as a forged attempt. The simulation forgery can be distinguished from the genuine signature, but it can still be identified as a forgery. The traced forgery could pass off as a genuine signature.