WIFS: Workshop on Information Forensics and Security 2022
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</div>
# Table of content
-[Overview](#overview)
-[Method](#code)
-[Dataset](#dataset)
-[Integrity check results](#results)
-[Citation](#citation)
-[Acknowledgements](#acknowledgements)
# Overview
This page contains the materials presented in the paper
[A Two-Step Method for Ensuring Printed Document Integrity using Crossing Number Distances](https://hal/WIFS2022_paper.pdf).
You can find here the [impementation](#code) of proposed method in Python 3 and the audmented PaySlip [dataset](#dataset).
# Method
The implementation of our method was done using Python and standard image processing libraries, such as OpenCV, matplotlib, and scikit-image.
# Dataset
The augmented PaySlip dataset can be downloaded [here](http:/.../AudmantedPaySlip.zip). We provide 100 document images printed and scanned using X priner/scanner at 600dpi resolution.
For our dataset, we have obtained the following results :
| Genuine | Forged
:--- | :---: | :----:
PS 300dpi | 100% | 95%
PS 600dpi | 100% | 90%
Double PS 600dpi | 50% | 100%
:--- | :---: | :----:
Total mean | 83% | 95%
# Citation
The code and the augmented dataset could only be used for scientific purposes. It must not be republished other than by the original authors. The scientific use includes processing the data and showing it in publications and presentations. If you use it, please cite:
```
@InProceedings{yriarte2022two,
author = {Yriarte F., Puteaux P. and Tkachenko I},
title = {A Two-Step Method for Ensuring Printed Document Integrity using Crossing Number Distances},
booktitle = {Proceedings of the IEEE International Workshop on Information Forensics and Security},
month = {December},
year = {2022}
}
```
# Acknowledgments
This work was funded by the project FuzzyDoc supported by the CNRS Research Group of Information, Signal, Image and Vision (CNRS GdR-ISIS).
