Simulated Forgeries Research Articles

Determination of Sequence of Strokes-Conventional & Non-Conventional Techniques - A Review Study

The most important tool for a document examiner is his or her own vision, but in this section and the laboratory tour section we will consider the external tools which enable the examiner to see, evaluates and record more than is apparent to the unaided eye. The number of papers outlines the basic necessity of photographic illustrations for demonstrating the data offer clinching evidence, strong enough to with stand legal scrutiny on which opinions of the document examiner are based, hence play a very vital role in the examination of forensic documents. For the purpose of examination in the laboratory as well as the use of photographs becomes indispensible in cases involving traced or simulated forgery, built-up documents, forgery over genuine signatures and forgery by substitution, interpolations, differences in the tint of ink etc. The authors have solved a case in resolving the sequence of strokes with same ball point ink in an actual case received in the laboratory.

Antitamper Image Watermarking Based on Cellular Network Topology for IoT‐Themed Mobile Forensics

The Internet of Things (IoT) connects physical and digital worlds with mobile devices, accompanied by a surge in cybersecurity issues. With the rapid adoption of mobile devices, mobile forensics emerges as a new interdisciplinary field that concerns many forms of sabotage and cybercrime in the context of mobile computing. One of the most common cyberattacks is tampering. Digital watermarking is a tamper‐evident technique used to protect data integrity. In this paper, we present an antitamper image watermarking scheme designed for mobile communications with low computational cost. A reference matrix based on cellular network topology is introduced to guide the watermark embedding and extraction processes. This reference matrix serves as a lookup table to reduce computational complexity, thereby enabling efficient implementation on mobile devices. Our scheme is aimed at offering high accuracy in detecting and localizing tampered regions. We also achieve a high watermarking capacity while leaving the visual quality of the carrier images nearly unharmed. Experimental results validate the effectiveness of our scheme against various types of simulated forgery including cropping and copy/paste attacks.

Depth-Dependent Chemical Analysis of Handwriting by Nanospray Desorption Electrospray Ionization Mass Spectrometry.

Nanospray desorption electrospray ionization (nano-DESI) has been utilized in direct sampling mass spectrometry (MS) that requires highly spatially resolved sampling with minimal sample destruction. In this study, we explored the applicability of nano-DESI MS for the forensic chemical analysis of ink directly from handwriting on paper. Nano-DESI readily ionizes dyes, including the polyanionic ones, with minimal fragmentation and produces chemical fingerprints of ballpoint pens directly from a paper surface. Further, we specifically focused on how the potential of nano-DESI that changes the mass spectral profiles over time could reflect the differential distribution of analytes in a vertical direction because mildly extracted analytes are immediately transferred and analyzed in real time. To test this, we wrote the character "X" with various combinations of two different pens and analyzed the crosspoints by nano-DESI MS. As a result, the time-course changes in the chemical fingerprints of the ink, which were consistent with the order of the pen strokes, were successfully obtained by nano-DESI MS in most cases. After confirming the capability of the depth-dependent analysis of nano-DESI MS, we analyzed a simulated forgery in which the original and forged writings were made before and after affixing a seal and clearly distinguished the two portions based on the time-dependent changes in the profile of the ink compound.

Damaging Similarities in Highly Skilled Signature Simulations - Their Detection and Authorship

Questioned document examination is a highly specialized and challenging field of study, because of the variety and complexity of problems that are referred to document examiners. Detection and identification of practiced simulated forgeries is an area of forensic science which requires the professional skill, capability and acumen of an examiner, every time he (or she) is called upon to examine and report a case of this nature. This is because firstly, the practiced simulations bear a striking semblance to the copied model and usually contains the handwriting features of the writers, the real person as well as the forger, though in varying degrees.Secondly, the skilled forger rarely leaves his own imprint in the forgery he committed. Most authorities on the subject have advised that the identification of authorship, in such cases, may be considered as an exception rather than the rule. Besides discussing and reviewing the work done in this regard, the authors have also presented and illustrated an intricate case study where the authorship of simulated signatures could be associated with the known handwriting of the suspected forger

Variation in Length of Signatures in Case of Simulated Forgery

Simulated or imitation forgery is one of the pervasive forgeries among the group of forgers, where genuine signature of signatory authority is available to forger and he attempts to execute by following the pictorial effect of the design of the signature by simply drawing the same. However, several factors are revealed during this act of forgery. Not every reproduction has a perfect evidence of poor line quality, retouching, and other “classic” features that may establish it as a fraud. Others, specifically those carried out when copying simple short signatures may have a line quality not very diverse from the signature and can be made without pen lifts, retouching, or tracing. In such cases, it may not be probable to opine with an extraordinary degree of confidence that the questioned writing is an imitation, but, according to its degree of inaccuracy, that exist, it may be apparent to postulate forgery.\

A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND K-NEAREST NEIGHBOR CLASSIFIERS IN THE OFF-LINE SIGNATURE VERIFICATION

The ANN is a new classification technique within the field of applied mathematics learning theory that has been applied successfully in pattern recognition applications like signature, face and speaker recognition, whereas the k-NN may be a non parametric technique used for classification of handwriting recognition and signature verification. This paper reports on a comparison of the 2 classifiers in off-line signature verification. For this purpose, associate acceptable learning and testing protocol was created to watch the potential of the classifiers to soak up intrapersonal variability and highlight social similarity using random, easy and simulated forgeries by finding the accuracy.

Paper Spray Mass Spectrometry for the Forensic Analysis of Black Ballpoint Pen Inks.

This article describes the use of paper spray mass spectrometry (PS-MS) for the direct analysis of black ink writings made with ballpoint pens. The novel approach was developed in a forensic context by first performing the classification of commercially available ballpoint pens according to their brands. Six of the most commonly worldwide utilized brands (Bic, Paper Mate, Faber Castell, Pentel, Compactor, and Pilot) were differentiated according to their characteristic chemical patterns obtained by PS-MS. MS on the negative ion mode at a mass range of m/z 100-1000 allowed prompt discrimination just by visual inspection. On the other hand, the concept of relative ion intensity (RII) and the analysis at other mass ranges were necessary for the differentiation using the positive ion mode. PS-MS combined with partial least squares (PLS) was utilized to monitor changes on the ink chemical composition after light exposure (artificial aging studies). The PLS model was optimized by variable selection, which allowed the identification of the most influencing ions on the degradation process. The feasibility of the method on forensic investigations was also demonstrated in three different applications: (1) analysis of overlapped fresh ink lines, (2) analysis of old inks from archived documents, and (3) detection of alterations (simulated forgeries) performed on archived documents. Graphical Abstract ᅟ.

Long range correlation of preceded pixels relations and application to off‐line signature verification

Lately, off-line signature verification systems have been reintroduced based on the idea of modelling the signature images with various relations among their pixels. In this paper, a modified version of the partially ordered set feature extraction procedure is presented by enabling distant range interactions between preceded relations of pixel groups. In this way, the spatial diversity of correlation that exists among signature features can be exploited. The motive behind this approach is related to our belief that the particular idiosyncratic writing style characteristics of each individual will be present over the whole length of the signature. Experiments involve the well-known Center of Excellence for Document Analysis and Recognition (CEDAR) and Ministerio de Ciencia y Tecnologia (MCYT) datasets in two popular writer dependent training modes: In the first mode, genuine and simulated forgeries were utilized, while in the second one genuine and random forgeries only were utilized during the training stage of the classifier. In both cases, the testing phase is exploited with genuine, simulated and random forgeries while receiver operating characteristic along with decision oriented FAR, FRR and average error metrics are assessing the proposed feature extraction method. The results obtained, show that the long range correlation of grid features can be efficiently employed for off-line signature verification.

A study of simulated forgery of signature

A signature (from Latin: signare, sign) is a handwritten (and often stylized) depiction of someone's name, nickname, or even a simple X documents as a proof of identity and intent. The writer of a signature is a signatory or srgner. Similar to a handwritten signature, a signature describes the work as readily identifying its creator. A simulated forgery is a simulation of a genuine writing. Variation is visible in Genuine and forged signature It is not always possible to depict by what method fraudulent writing was made but it shows some light on the investigation .The study on 70 simulated forgery shows that weak signatures can be pictorial or rhythamatically forged, can mislead to anyone but by using sophisticated tool the signature which are simulated with much accuracy can be detected and examined. Variability by some measures likes slant, loop, angle, baseline and strokes etc. This investigation protocol will help to prevent from forgery and personal damage and money loss.

Examination of Simulated Forgery and its Investigation Protocol

Every day, we may face signature verification problem directly or indirectly whether it is in a banking transaction or signing a credit card transaction or authenticating a legal document. Signature identification and the questions of disguise, forgery and genuineness account for a significant proportion of a document examiner's case load. The development and examination, of a personal signature follows almost all of the concepts relating to handwriting. General indications of non-genuineness may include blunt starts and stops, pen lifts, hesitations, tremor, speed, pressure and patching. Forgery is an illegal modification or reproduction of an image, document signature, legal tender or any other means of recording information. Now-a-days, forgery detection is very important for real world events. In the present study 3 specimen of signature were collected from 50 individuals and were simulated for the examination of simulated forgery and its investigation protocol with Video Spectral Comparator.

Grid-based feature distributions for off-line signature verification

In this work, a feature extraction method is presented for handwritten signature verification. The proposed algorithm models the handwritten elements of a signature trace by probabilistically counting the distribution of fixed two- and three-step pixel paths, conditioned that they are confined within predetermined Chebyshev distances of two and three, respectively. This representation correlates the pixel transitions along the signature trace, with the writing style of an individual. Various partitions of the signature image into a group of sub-images were applied in order to define the overall dimensionality of the feature. In order to evaluate the classification efficiency of the introduced method, a number of verification strategies are implemented by making use of two internationally accepted and one domestic datasets. In all schemes, similarity scores and hard margin support vector machines (SVMs) are combined or evaluated as separate entities. Additionally, zoning the extracted feature vector into combinations of tetrads and heptads, which in turn are fed into the afore-mentioned classification schemes, is exploited. Results, derived from random or simple imitations as well as simulated (skilled) forgery indicate that the proposed method achieves noticeably low equal error rates and it is expected to provide a powerful discriminative representation of the handwritten signature.

Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers

In this work we address two important issues of off-line signature verification. The first one regards feature extraction. We introduce a new graphometric feature set that considers the curvature of the most important segments, perceptually speaking, of the signature. The idea is to simulate the shape of the signature by using Bezier curves and then extract features from these curves. The second important aspect is the use of an ensemble of classifiers based on graphometric features to improve the reliability of the classification, hence reducing the false acceptance. The ensemble was built using a standard genetic algorithm and different fitness functions were assessed to drive the search. Two different scenarios were considered in our experiments. In the former, we assume that only genuine signatures and random forgeries are available to guide the search. In the latter, on the other hand, we assume that simple and simulated forgeries also are available during the optimization of the ensemble. The pool of base classifiers is trained using only genuine signatures and random forgeries. Thorough experiments were conduct on a database composed of 100 writers and the results compare favorably.

Automatic Authentication of Handwritten Documents via Low Density Pixel Measurements

the skilled forgeries, often subclassified into traced and simulated forgeries, involve attempting to mimic the style of the In this paper, we propose an effective approach of automatic writer and thus can be difficult to detect with only these static handwriting authentication via low density pixel measurements features. The main problem comes in designing a feature ex- with an adaptive threshold as decision boundary. For simplictraction method which gives stable features for the genuine ity, we deal only with handwritten signatures in the proposed written samples despite their inevitable variations, and salient scheme, which can well be extended to any piece of handwritfeatures for the forgeries even if the imitations are skillfully ten samples without the loss of generality. Towards this, the done. Even though a genuine writer can never produce ex- low and high density pixel percentages are computed and ten actly the same handwriting twice, e.g., signatures, and many effective features are proposed along with the ratio of above factors can affect signatures and other handwriting including mentioned density percentages. An adaptive decision criteria

A comparison of SVM and HMM classifiers in the off-line signature verification

The SVM is a new classification technique in the field of statistical learning theory which has been applied with success in pattern recognition applications like face and speaker recognition, while the HMM has been found to be a powerful statistical technique which is applied to handwriting recognition and signature verification. This paper reports on a comparison of the two classifiers in off-line signature verification. For this purpose, an appropriate learning and testing protocol was created to observe the capability of the classifiers to absorb intrapersonal variability and highlight interpersonal similarity using random, simple and simulated forgeries.

Identifying Simulations: Practical Considerations

Abstract The characteristics of simulated forgeries and the conditions that allow for the comparison of simulations with handwriting of suspected forgers are discussed.

Identifying the Copying Machine Used in Preparation of Simulated Forgeries

Abstract The proliferation of rapid office copiers has in recent years provided a new problem for Document Examiners. Not only is it possible to determine which of many processes was used to prepare a given copy, in many cases an individual machine can be identified as the one on which the copy was made. One of the more common types of office copiers, the xerograph machine, lends itself to these sorts of identification quite well. To establish that a copy was in fact made on a particular copying machine requires full analysis of the copy and the machine.