Python implementation of Benford's Law tests.
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Updated
Oct 11, 2022 - Jupyter Notebook
Python implementation of Benford's Law tests.
benfordslaw is about the frequency distribution of leading digits.
A framework for Benford's Law conformity assessment.
[📊] Statistical analysis of over one million lines of death toll data released by countries around the globe. We then applied our algorithm and compared it to the curve of Benford's Law to detect under-reporting and false death data.
This project focuses on detecting a specific form of image forgery known as a copy-move attack, in which a portion of an image is copied and pasted elsewhere.
An experiment to demonstrate the biases and predictability of our world.
Experiment with Benford's Law to find data anomalies in number lists and images (Windows, macOS, Linux, .NET 8.0, x64, Arm64, Apple Silicon)
Official repository for the paper "On the use of Benford's law to detect GAN-generated images", ICPR2020
Project that aims to identify credit card transaction fraud, through the implementation of various supervised machine learning models.
A short script written in Python to apply Benford's law and Chi-squared test in practice.
this project show to use the library benford-analysis
Check out the thesis "Did 2001 Mark the Beginning of a More Manipulated Market? An Analysis of Financial Markets via Benford's Law" by Richard Wright and Erik Munther. http:https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54686
A mathematical law that predicts the frequency distribution of leading digits yields incredible results on the Youtube statistics dataset!
Benford's Law applied to COVID-19 cases
This repo implements and graphs ben-fords law from csv and json files
Benford's Law is an application designed to take user-supplied numerical data and generate a report to show if the data set follows Benford's Law. https://en.wikipedia.org/wiki/Benford's_law
This program acts as a customer information and sales analysis system. By using the Luhn Algorithm and Benford's law, this program can validate credit card information and determine fraud by reading transaction histories, which can be visualized through the use of JavaFX
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