Identity theft

About: Identity theft is a research topic. Over the lifetime, 2284 publications have been published within this topic receiving 31700 citations.

A New Approach Based on Steganography to Face Facial Recognition Vulnerabilities Against Fake Identities

Abstract: By the huge use of biometric identification and authentication systems, securing user’s images is one of the major recent researches topics. In this paper we aim to present a new approach which is based on detecting face in any picture (even with low quality) a detection that can goes to 93%. As a second work we aim to test the ability of this algorithm to identify people while wearing medical masks. In fact, with the spread of Covid-19, people are now obliged to wear medical masks. These masks cover almost 60% of persons faces, this lack of information can prevent the identification of the person or can create some confusions. And To secure images and prevent identity theft we propose an approach that consists of hiding a generated key in each person’s image. This unique key is based on user’s personal information, the key will be verified by Luhn algorithm, which is considered as a widely used algorithm to verify generated IDs. As a third work, we aim to hide the person’s ID in the image using a steganographic algorithm. Our work main objective is to protect pictures and prevent any attempt of creating a fake model of the owners. Therefore, the ID hidden in the picture will be destructed in every attempt of creating a fake image or model.

Role of Data Mining in Detecting Theft and Making Effective Impact on Performance Management

Abstract: AbstractThis paper analyses the effectiveness of DM (Data Mining) classification techniques for company detection. Different approaches of data mining are accessible for the process of data mining. In many applications, data mining techniques are used to identify and prevent various kinds of theft. While data mining research and possible measures for the detection and identification of different forms of theft are already in progress, there is limited study that reconstructs many elements of theft that employ data mining methods. The use of data mining techniques to detect theft is a responsibility. We are also classifying theft into three groups with respect the use of data mining as an aid for the identification and prevention. This report examines the efficacy in determining false financial statements of policymakers, artificial neural networks, and Bayesian beliefs networks. Management theft, Theft against Customers, and computer-based theft are all three kinds of theft.KeywordsData miningTheftManagementManagementCustomerComputer

Do Commonly Recommended Preventive Actions Deter Identity Theft Victimization? Findings from NCVS Identity Theft Surveys

Abstract: It remains unknown if taking commonly used preventive actions is related to identity theft. In the current study, we use a dataset featuring over 220,000 respondents to the National Crime Victimization Survey Identity Theft Supplement (NCVS ITS). The survey was conducted by the Bureau of Justice Statistics (BJS) first in 2012, then again in 2014, and once more in 2016. The findings reported here suggest that demographic variables (e.g., gender, income) and types of online activities (e.g., frequency of online shopping) are significantly related to identity theft victimization. An interesting additional finding is that among seven distinct types of preventive actions listed in the NCVS ITS survey (frequently checking credit reports, frequently changing passwords for financial accounts, employing purchase credit monitoring, shredding documents containing personal information, monitoring bank statements for suspect charges, using security software programs, and purchasing identity theft protection), shredding documents with personal information ALONE is significantly negatively related to identity theft victimization. All six other preventive actions are either positively related or unrelated to identity theft victimization. These findings generate practical implications and, most importantly, raise the question of whether some newly-fashioned preventive actions might provide better protection from identity theft protection.