Image Processing Based Signature Verification Technique to Reduce Fraud in Financial Institutions

Hussein, Walid; Ibrahim, Osman

doi:10.1051/matecconf/20167605004

Cited by 7 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For these reasons, we chose an experimental task where participants had to verify signatures against a reference signature. This task is highly relevant as it is part of many business processes (e.g., account opening, buying an insurance policy, or contract amendment) and is of great interest to organizations to reduce fraud (e.g., Hussein et al, 2016). Moreover, the use of images can reduce any kind of language-related bias, as no understanding of a specific language is required to fulfill this task.…”

Section: Experimental Taskmentioning

confidence: 99%

Explanation matters: An experimental study on explainable AI

Hamm,

Klesel,

Coberger

et al. 2023

Electron Markets

View full text Add to dashboard Cite

Explainable artificial intelligence (XAI) is an important advance in the field of machine learning to shed light on black box algorithms and thus a promising approach to improving artificial intelligence (AI) adoption. While previous literature has already addressed the technological benefits of XAI, there has been little research on XAI from the user’s perspective. Building upon the theory of trust, we propose a model that hypothesizes that post hoc explainability (using Shapley Additive Explanations) has a significant impact on use-related variables in this context. To test our model, we designed an experiment using a randomized controlled trial design where participants compare signatures and detect forged signatures. Surprisingly, our study shows that XAI only has a small but significant impact on perceived explainability. Nevertheless, we demonstrate that a high level of perceived explainability has a strong impact on important constructs including trust and perceived usefulness. A post hoc analysis shows that hedonic factors are significantly related to perceived explainability and require more attention in future research. We conclude with important directions for academia and for organizations.

show abstract

Section: Experimental Taskmentioning

confidence: 99%

Explanation matters: An experimental study on explainable AI

Hamm,

Klesel,

Coberger

et al. 2023

Electron Markets

View full text Add to dashboard Cite

show abstract

“…But average returns the average of all values from the part of the image that the kernel covers. To improve the accuracy of signature verification, some studies [8,9,10] use machine learning techniques, which are one of the most notable technologies. Buriro et al [3] used a multilayer perceptron (MLP), a two-class classifier, to verify a finger-drawn signature with dynamic features involving finger and phone movements.…”

Section: Our Studies Includedmentioning

confidence: 99%

Advancing Security: Machine Learning-Based Signature Forgery Detection in Document Authentication Systems

2023

IJFMR

View full text Add to dashboard Cite

Handwritten signatures play a vital role in our lives. From banks to institutions to organizations, signatures are a way of identifying a person. However, signings come with a lot challenges because any two signatures can look very similar with slight differences written the same person. Therefore, the identification of real and fake signatures is very difficult. To avoid similar identity related crimes committed in banks and many others companies, the counterfeit detection system is the solution to this problem along with the help concepts of machine learning and CNN. For better performance and time efficiency, Parallelization concepts are used in software implementation. This software can be used to verify signatures on many platforms such as loans, signing legal documents, applications signing, applications and much more.

show abstract

“…During the literature review, the priority was given to more modern approaches to establish a solution to prohibit users publishing downloaded images from the internet as their own .The research done by Walid Hussein, Osman Ibrahim and Mostafa A. Salama [2] on image processing using the signature verification techniques reviews how signature verification techniques can be done using an image.…”

Section: A Analysis Of the Usage Of Metadatamentioning

confidence: 99%

Hashtag Generator and Content Authenticator

Abeywardana¹,

Ginige²,

Herath³

et al. 2018

ijacsa

View full text Add to dashboard Cite

In the recent past, Online Marketing applications have been a focus of research. But still there are enormous challenges on the accuracy and authenticity of the content posted through social media. And if the social media business platforms are considered, majority of the users who try to add a market value to their own product face the problem of not getting enough attention from their target audience. The purpose of this research is to develop a safe and efficient trending hashtag generating application solution for social media business users which generates trending and relevant hashtags for user content in order to get a broad reach of target audience, automatically generates a meaningful caption to their relevant posts and guarantees the authenticity of the product at the same time. The user content is analyzed and filters the important keywords, generates a meaningful caption, suggest related trending keywords and generates trending hashtags to get the required reach for online marketers. Additionally, the marketing products' content authentication is ensured. The application uses Natural Language Processing, Machine Learning, API technologies, Java and Python technologies. A unique database is assigned to users which contains rankings for each user. The target audience who engages in buying products get to know about the status of the sellers with respect to authenticity of the content. It is believed that the application provides a promising solution to existing audience reach problems of online marketers and buyers. The significance of this system is to help marketers and buyers to engage in online buying and selling with much effective, reliable and safer ways. This mitigate the vulnerability of bad social media marketing influences and helps to establish a safe and reliable online marketing practice to make both sellers and buyers happy. This paper provides a brief description on how to perform an organized online marketing discipline via the Trending Hashtag Generator & Image Authenticator application.

show abstract

Image Processing Based Signature Verification Technique to Reduce Fraud in Financial Institutions

Cited by 7 publications

References 10 publications

Explanation matters: An experimental study on explainable AI

Explanation matters: An experimental study on explainable AI

Advancing Security: Machine Learning-Based Signature Forgery Detection in Document Authentication Systems

Hashtag Generator and Content Authenticator

Contact Info

Product

Resources

About