摹仿逝者少量字签名的鉴定方法与价值特征分析

通过分析一起摹仿逝者签名笔迹的案件鉴定,初步探讨摹仿少量字签名的基本特征,在此基础上归纳出伪造少量字签名笔迹特征的基本规律,从签名笔迹的概貌特征和细节特征之中运用辩证的观点做出准确的结论。     

浅谈签名类笔迹的鉴定

一、签名笔迹案件的特点签名笔迹字数少,笔迹特征数量有限,检验难度一般很大,而且大部分为利用摹仿手法伪装的签名,这种伪装方法暴露作案人的书写习惯比较少,通过笔迹鉴定很难认定摹仿书写人。常见的签名方式有横式、竖式和倾斜式三种。一些人忙于手中事务,不管要签字的文件怎样放置,有时甚至看都不看一眼就签上了名字。这样,纸张与签名形成的角度特征极不稳定,利用价值降低。人们在设计自己的签名时,经常会造成某种特殊的视觉效果,同时增加了他人伪造自己签名的难度,防止他人伪造自己的签名。如某案中“冯某某”的签名,三个字…     

浅谈对签名笔迹的检验

1 签名笔迹的特点 签名笔迹与其它笔迹相比较,有其独自的特点:1.签名笔迹的案由都是涉及到经济问题;2.签名笔迹字数少(两、三个字);3.签名笔迹的相同字、同名部首及同名笔画少;4.签名笔迹重复出现的笔迹特征少;5.签名笔迹可比性强的特征少。 2 对签名笔迹检验的方法 对签名笔迹检验的方法,是遵循同一认定的基本原理,对送检文字材料的笔迹特征同嫌疑人     

论签名笔迹的频谱分析方法

通过一种签名笔迹的频谱分析方法,并提取签名笔迹的压力、速度等动态特征数据,利用傅立叶变换原理,对特征数据进行频谱分析。实验研究发现,同一人的签名笔迹频谱图相似度高,且明显区别于他人的签名笔迹。签名笔迹的频谱分析方法可以直观、有效的鉴别签名笔迹。     

签名字迹的检验

签名字迹是指在公私文书上签署自己姓名而形成的文书笔迹符号。文书上的签名是文书真实性和有效性的标志和证明,于是伪造签名也就成为不少违法犯罪分子的惯用伎俩,尤其在经济犯罪案件中的伪造签名更多。也正因为如此,为了从法律上明确文书签名的真实性和有效性,确定文书中可疑签名字迹的真伪和书写人,就产生了签名字迹鉴定。1真实签名字迹的特点(1)字数少,动作的规律性表现不充分。签名通常是两个字、三个字或四个字,相同单字、组成部分、笔画等整体特征的重复率低,因此,书写习惯暴露不充分,书写规律不易掌握。(2)签名字迹具有相对的不稳定…     

浅析摹仿签名笔迹鉴定

摹仿签名笔迹检验是笔迹检验的难点,认定签名摹仿人更是难中之难;笔者通过正确确定签名笔迹的真伪,从签名笔迹的概貌特征和细节特征之中运用辩证的观点正确认定摹仿人。     

从一起签名鉴定看中西方笔迹专家的不同理念

签名笔迹鉴定个案引发的中西方笔迹专家不同理念的碰撞,促使本文在中西文签名笔迹相比较的基础上,对中西方笔迹专家在签名鉴定方面的理念差异进行初步探讨。     

再看摹仿签名笔迹鉴定

由于签名笔迹字数少、可摹仿性强的特点,长期以来,摹仿签名笔迹鉴定始终是笔迹鉴定的难点。笔者从不同类型的摹仿方法出发,讨论不同方法下摹仿签名笔迹的特点,总结其特征规律。同时要重视笔迹鉴定过程中了解相关案情,为识别是否存在摹仿现象提供线索。在此基础之上,对比摹仿签名笔迹特征的变化规律,并从摹仿签名笔迹特征反映构成方面系统性地进行鉴定,鉴别是否存在摹仿签名的情况。通过撰写此文,以期对摹仿签名笔迹鉴定实务具有借鉴意义。     

写字机器人伪造签名字迹的鉴别

目的通过实验研究使用写字机器人伪造的签名字迹特征,减小文件检验鉴定中的错判风险。方法以真实书写字迹为模板、使用市面上主流款式的写字机器人执不同种类书写笔进行书写,通过直接观察、显微观察、反射变换成像(RTI)方法观察等方式比对手写字迹和机器人书写字迹的差异,归纳写字机器人伪造字迹的主要特征,总结出有效的检验鉴别方法。结果通过研究发现写字机器人伪造签名笔迹的特点主要有起收笔为顿压笔,笔画出现细微的抖动歪曲现象,整体笔画笔压均匀,运笔生硬无笔势等。结论目前写字机器人伪造签名字迹与真实手写字迹还有一定差异,使用反射变换成像(RTI)方法观察能达到较好的检验效果。     

浅谈摹仿老年人签名笔迹鉴定

笔迹鉴定中,签名笔迹的鉴定是一直是鉴定中的难点,而摹仿老年人签名笔迹的鉴定,由于把老年人的笔迹特点与摹仿伪装笔迹的特点糅合在了一起,更成为了难点中的难点。为此,笔者查阅相关资料和结合自身鉴定实践,对摹仿老年人签名笔迹的特点以及鉴定依据和方法进行一些探讨。     

利用真签名伪造文书的识别与检验

通常情况下,绝大多数人认为,签名真伪决定了文书真伪。司法鉴定实际工作中,事实上签名真伪与文书真伪并不是等同关系。利用真签名伪造文书有许多方法。由于签名真是书写人的笔迹,因此在利用真签名伪造文书的实际鉴定中,鉴定人往往忽视系统检验,从而作出不符合客观实际的鉴定结论。在检验中,应充分根据利用真签名伪造文书不同方法出现的不同特点,综合运用案情分析法、细节特征比较法、图像软件分析法、仪器检验法、系统检验法进行检验。在检验过程中,还应注意受理案件时的材料是否为原件;案件情况中双方当事人矛盾的焦点;签名是真签名还是摹仿签名。     

Identification of Opioids and Related Substances using Handheld Raman Spectrometers

This study describes the performance of handheld Raman devices for detecting one hundred opioids and related substances including fentanyl and several analogs. Using a single “parent” device, signatures (spectra) with excellent signal-to-noise ratios were generated using <5 mg of most compounds. The signatures were added to a method (library), which was electronically transferred to three “daughter” devices. The devices were able to discriminate different salt forms and isomers. On average, the daughter devices yielded a true-positive rate of 97.3% for generating an alarm for opioids and were 93.3% effective for correctly identifying the opioid. The devices yielded true-negative, false-positive and false-negative rates of 100%, 0%, and 2.7%, respectively, where false negatives were due to weak signal and fluorescence. These data demonstrate that the parent-daughter electronic transfer method was successful and effective, which permits the ability to develop methods in the laboratory that can be seamlessly pushed out to field devices.     

Are expert penmen better than lay people at producing simulations of a model signature?

Although forensic signature examination is considered to be an identification science, it is a theoretical possibility that an individual may learn to forge another person's signature that is free from indications of simulation behaviours. This proposition was tested in a signature blind trial that was administered to 42 forensic handwriting examiners (FHEs). Participants expressed opinions on the authorship of 100 questioned signatures. The questioned signatures comprised a mixture of genuine, disguised and simulated signatures. Calligraphers formed part of the population of individuals who provided simulated signatures for the trial. A total of 3100 opinions were expressed of which 1254 were correct, 224 misleading and 1622 were inconclusive. Of the opinions expressed regarding the simulated signatures, the misleading score for the calligraphers' forgeries were approximately four times that of the lay persons' forgeries. These results provide strong evidence in support of the proposition that calligraphers are more skilled at simulating signatures than are lay people and can produce forgeries that some FHEs have difficulty detecting.     

The probative character of Forensic Handwriting Examiners' identification and elimination opinions on questioned signatures

This 5-year study investigated the character of Forensic Handwriting Examiners' (FHEs) authorship opinions on questioned signatures through the medium of blind validation trials. Twenty-nine thousand eight hundred and eleven authorship opinions were expressed by FHEs on trial kits comprising randomized questioned genuine signatures (written by the specimen writer), disguised signatures (written by the specimen writer) and simulated signatures (not written by the specimen writer). Results showed that, as a group, FHEs were significantly more confident at identifying writers' genuine signatures than identifying writers' disguised signatures or eliminating specimen writers from having authored simulated signatures. It is proposed that the difference in FHE confidence arises from the difficulty they have in deciding which alternative authorship explanation accounts for perceived combinations of similar and dissimilar features between specimen and questioned signatures.     

The probative character of Forensic Handwriting Examiners’ identification and elimination opinions on questioned signatures

This 5-year study investigated the character of Forensic Handwriting Examiners’ (FHEs) authorship opinions on questioned signatures through the medium of blind validation trials. Twenty-nine thousand eight hundred and eleven authorship opinions were expressed by FHEs on trial kits comprising randomized questioned genuine signatures (written by the specimen writer), disguised signatures (written by the specimen writer) and simulated signatures (not written by the specimen writer). Results showed that, as a group, FHEs were significantly more confident at identifying writers’ genuine signatures than identifying writers’ disguised signatures or eliminating specimen writers from having authored simulated signatures. It is proposed that the difference in FHE confidence arises from the difficulty they have in deciding which alternative authorship explanation accounts for perceived combinations of similar and dissimilar features between specimen and questioned signatures.     

纸上静态签名笔迹动态特征的提取和分析

目的建立一种新型的定量化签名笔迹鉴别技术。方法建立签名笔迹样本库，导入计算机后，提取签名笔迹的宽度，灰度和弧度等动态特征数据，进行时间归整后，比较签名动态特征之间的相关系数及其规律。结果同一人签名的动态特征之间相关系数高，而与代签签名、临摹签名和套摹签名均有显著差异。结论本研究开发和建立了一整套检测、提取和分析纸上签名笔迹动态特征的工具和方法，该方法能有效鉴别本人签名和非本人签名。     

The influence of gender on ability to simulate handwritten signatures: a study of Arabic writers

This study investigates whether a writer's gender can be determined from an inspection of simulated signatures written in the Arabic alphabet or Arabic abjad. It is generally believed that the penmanship of female writers is superior to male writers. There is also reason to expect that superiority in writing skill might contribute to success in simulating the signatures of other writers. Simulated signatures produced by a large population of male (414) and female (312) Arabic writers were graded, and the results were statistically analyzed. Women were found to have a marginal advantage simulating all elements of the signatures, but there was no statistically significant difference between the genders on any of the elements examined.     

Integrity verification of the ordered data structures in manipulated video content

Video content stored in Video Event Data Recorders (VEDRs) are used as important evidence when certain events such as vehicle collisions occur. However, with sophisticated video editing software, assailants can easily manipulate video records to their advantage without leaving visible clues. Therefore, the integrity of video content recorded through VEDRs cannot be guaranteed, and the number of related forensic issues increases. Existing video integrity detection methods use the statistical properties of the pixels within each frame of the video. However, these methods require ample time, because they check frames individually. Moreover, the frame can easily be replaced and forged using the appropriate public software. To solve this problem, we propose an integrity checking mechanism using the structure of ordered fields in a video file, because existing video editing software does not allow users to access or modify field structures. In addition, because our proposed method involves checking the header information of video content only once, much less detection time is required compared with existing methods that examine the entire frames. We store an ordered file structure of video content as a signature in the database using a customized automated tool. The signature appears according to the video editing software. Then, the suspected video content is compared to a set of signatures. If the file structure matches with a signature, we recognize a manipulated video file by its corresponding editing software. We tested five types of video editing software that cover 99% of the video editing software market share. Furthermore, we arranged 305,981 saving options for all five video editing suites. As a result, we obtained 100% detection accuracy using stored signatures, without false positives, in a collection of 305,981 video files. The principle of this method can be applied to other video formats.