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%.

Text-Independent Algorithm for Source Printer Identification Based on Ensemble Learning

Because of the widespread availability of low-cost printers and scanners,document forgery has become extremely popular.Watermarks or signatures are used to protect important papers such as certificates,passports,and identification cards.Identifying the origins of printed documents is helpful for criminal investigations and also for authenticating digital versions of a document in today’s world.Source printer identification(SPI)has become increasingly popular for identifying frauds in printed documents.This paper provides a proposed algorithm for identifying the source printer and categorizing the questioned document into one of the printer classes.A dataset of 1200 papers from 20 distinct(13)laser and(7)inkjet printers achieved significant identification results.A proposed algorithm based on global features such as the Histogram of Oriented Gradient(HOG)and local features such as Local Binary Pattern(LBP)descriptors has been proposed for printer identification.For classification,Decision Trees(DT),k-Nearest Neighbors(k-NN),Random Forests,Aggregate bootstrapping(bagging),Adaptive-boosting(boosting),Support Vector Machine(SVM),and mixtures of these classifiers have been employed.The proposed algorithm can accurately classify the questioned documents into their appropriate printer classes.The adaptive boosting classifier attained a 96%accuracy.The proposed algorithm is compared to four recently published algorithms that used the same dataset and gives better classification accuracy.

The Hot Issues and Future Direction of Forensic Document Examination in China

The domain of forensic document examination consists of a variety of professional tasks related to the history and preliminary of questioned documents.The wide area of tasks implemented by forensic document examiners includes the skill to identify the authorship of handwriting and signature;discriminate among genuine,imitated,or disguised handwriting;to examine and analyze components of inks,papers,and other materials connected with documents;and other highly skilled analyses based on specialized technique.The field of handwriting examination is tightly related to empirical knowledge and practical experience.Handwriting is usually thought to be behavioral activities that are identifiable due to the appearance of features and characteristics within the writing.When we assess these features we universally analyze them in two aspects:Both quality and quantity of writing.As mentioned above,the evaluation of quality and quantity is performed by forensic document examiners.The problem is how to conduct this kind of evaluation related to empirical knowledge and experience.There is not consistent answer yet,although many researchers devote themselves to explore in this hot issue,such as mathematical methods applied in handwriting analysis.In this article,several mathematical methods applied to forensic document field are deciphered in domestic perspective of China.In the digital age,we all inevitably deal with problems with the help of computer or computer technology.We also pay close attention to the computer technique assistant in the field of forensic document examination.Standards or criteria of forensic document examination are still hot issues in China.Although we established a few standards or criterion based on requires of practice,it is also a lack of standards or criterion associated to trainees and continuing education in view of practitioners.At the end of this article,the author intends to discuss a piece of a challenge to forensic document examination,which are also universal issues abroad.Those who are critical of the current state of knowledge in this area argue that members of the judiciary have failed to sufficiently address the shortcomings of the forensic document evidence proffered in criminal trials.Specifically,critics of forensic document examination argue that little valuable wording exists in the report to support the validity and reliability of the methodology and findings of forensic document examination.

A hyperspectral unmixing approach for ink mismatch detection in unbalanced clusters

Detecting ink mismatch is a significant challenge in verifying the authenticity of documents,especially when dealing with uneven ink distribution.Conventional imaging methods frequently fail to distinguish visually similar inks.Our study presents a novel hyperspectral unmixing approach to detect ink mismatches in unbalanced clusters.The proposed method identifies unique spectral characteristics of different inks employing k-means clustering and Gaussian mixture models(GMMs)to perform color segmentation on different ink types and utilizes elbow estimation and silhouette coefficient to evaluate the number of inks estimation precisely.For a more accurate estimation of quantity,which is generally not an attribute of clustering methods,we employed entropy calculations in the red,green,and blue depth channels for precise abundance estimation of ink.This unique combination of basic techniques in conjunction exhibits better efficacy in performing ink unmixing and provides a real-world document forensic solution compared to current methods that rely on assumptions like prior knowledge of the inks used in a document and deep learning-based methods that rely heavily on abundant training datasets.We evaluate our approach on the ivision handwritten hyperspectral images dataset(iVision HHID),which is a comprehensive and rich dataset that surpasses the commonly-used UWA writing inks hyperspectral images(WIHSI)database in size and diversity.This study has accomplished the unmixing task with three main challenges:unmixing of diverse ink spectral signatures(149 spectral bands instead of 33 bands in the previous dataset),without using prior knowledge and assumptions about the number of inks used in the questioned document,and not requiring large training data for performing unmixing.Furthermore,the security of the proposed document authentication methodology to address the likelihood of forgeries or manipulations in questioned documents is enhanced as compared to previous works relying on known inks and known spectrum.Randomization techniques and anomaly detection mechanisms are used in our methodology which increases the difficulty for adversaries to predict and manipulate specific aspects of the input data in questioned documents,thereby enhancing the robustness of our method.The code for conducting this research can be accessed at GitHub repository.

An examination of handwritten signatures forged using photosensitive signature stamp

Signature examination is the most common examination performed by any document examiner.Determination of the authenticity of a handwritten signature on a questioned document is an important task for forensic document examiners in the forensic science field.As a result of continuous developments in technology,a signature stamp can now be created using a photosensitive seal to enable the reproduction of a handwritten signature.These stamps are commonly used in China and several other countries.In this study,10 types of black photosensitive stamp-pad ink,10 brands of fountain pen ink,15 types of black gel ink and six types of black erasable gel ink found on the Chinese domestic market were collected and 10 photosensitive signature stamps were created using the signatures of 10 people.Microscopic analysis,infrared(IR)and fluorescence analyses and microspectrophotometry(MSP)techniques were used to examine the resulting photosensitive signature stamp impressions when applied to printing papers,writing papers and invoice papers.By comparing the printing and spectral characteristics of the photosensitive signature stamp impressions with those of the signatures executed using the fountain pens,gel pens and erasable gel pens,it was possible to determine whether each signature was written or stamped using a photosensitive signature stamp.To validate these results,a 96.7%absolute accuracy and a 99.3%detection rate were achieved over a total of 150 blind tests conducted by five forensic document examiners,thus demonstrating that a combination of the four analysis methods used in this work can provide a more scientific approach and improve the accuracy and the detection rate of the examination process.