FHAG-with-BOIL
Overview
This is the implementation of the method proposed in "Distortion Model based Spectral Augmentation for Generalized Recaptured Document Detection" with pytorch(1.9.0, gpu version). The associated datasets are available upon request.
Introduction
In this work, we improve DPAD approaches by addressing two limitations of existing FDA methods. We establish a BOIL method that locates the BOI related to the recapturing operation and propose a FHAG strategy that enhances halftoning features in the BOI.
Environment Request
python == 3.10.6
fire == 0.4.0
matplotlib == 3.3.4
numpy == 1.20.2
opencv_python == 4.5.2.54
Pillow== 9.4.0
scikit_learn == 1.2.1
torch == 1.9.0
torchnet == 0.0.4
torchvision == 0.10.0
tqdm == 4.61.1
Files structure
- data
- models
- utils
- config.py
- main.py
- FHAG.py
- BOIL.py
- analyze.py
Files Description
BOIL.py
Calculate the BOI of existing sample
FHAG.py
Augment the recapturing features by amplitude manipulation
Run
Train
python main.py train Test
python test.py Analyze
python analyze.pyCitation
If you use our code please cite: Changsheng Chen, Bokang Li, Rizhao Cai, Jishen Zeng, and Jiwu Huang, Distortion Model based Spectral Augmentation for Generalized Recaptured Document Detection, in IEEE Transactions on Information Forensics and Security, 2023
The datasets presented in our work are available upon request.