Repository: grip-unina/noiseprint
Branch: master
Commit: c06034eedc92
Files: 199
Total size: 28.1 MB
Directory structure:
gitextract_pvls8abb/
├── .gitignore
├── LICENSE.txt
├── README.md
├── demo/
│ ├── demo_extraction.sh
│ ├── demo_heatmap.sh
│ └── outs/
│ ├── NC2016_2564_map.mat
│ ├── faceswap_map.mat
│ ├── faceswap_np.mat
│ ├── inpainting_np.mat
│ ├── seamcarving_map.mat
│ ├── splicing_map.mat
│ └── splicing_np.mat
├── docs/
│ ├── _config.yml
│ └── index.md
├── main_blind.py
├── main_extraction.py
├── main_map2uint8.py
├── main_showout.py
├── main_showres.py
├── noiseprint/
│ ├── __init__.py
│ ├── feat_spam/
│ │ ├── __init__.py
│ │ ├── mapping.py
│ │ ├── residue.py
│ │ └── spam_np_opt.py
│ ├── nets/
│ │ ├── net_jpg100/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg101/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg51/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg52/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg53/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg54/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg55/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg56/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg57/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg58/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg59/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg60/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg61/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg62/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg63/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg64/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg65/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg66/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg67/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg68/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg69/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg70/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg71/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg72/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg73/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg74/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg75/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg76/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg77/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg78/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg79/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg80/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg81/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg82/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg83/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg84/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg85/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg86/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg87/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg88/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg89/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg90/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg91/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg92/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg93/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg94/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg95/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg96/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg97/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ ├── net_jpg98/
│ │ │ ├── model.data-00000-of-00001
│ │ │ ├── model.index
│ │ │ └── model.meta
│ │ └── net_jpg99/
│ │ ├── model.data-00000-of-00001
│ │ ├── model.index
│ │ └── model.meta
│ ├── network.py
│ ├── noiseprint.py
│ ├── noiseprint_blind.py
│ ├── post_em.py
│ ├── requirements-cpu.txt
│ ├── requirements-gpu.txt
│ └── utility/
│ ├── __init__.py
│ ├── gaussianMixture.py
│ └── utilityRead.py
└── training/
├── LICENSE.txt
├── README.md
├── code/
│ ├── FCnet.py
│ ├── Producer2.py
│ ├── create_data.py
│ ├── db_utility.py
│ ├── train_denoiser.py
│ ├── train_noiseprint.py
│ └── train_utility.py
└── dataset/
├── README.txt
├── download_images.py
├── list_images.csv
└── list_models.csv
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
================================================
FILE: LICENSE.txt
================================================
THIS DOCUMENT CONSTITUTES A LICENCE TO USE THE SOFTWARE ON THE TERMS AND CONDITIONS APPEARING BELOW.
Preamble
This License applies to the software with which this license is distributed.
The software is intellectual property of Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA')
and is placed under the protection of copyright laws, including Italian legislation and international treaties.
BY USING THE PROGRAM, YOU INDICATE YOUR ACCEPTANCE OF THIS LICENSE TO DO SO.
Terms and Conditions
Reproduction, modification, and usage of the software covered by this license is allowed free of charge provided that:
(i) this software should be used, reproduced and modified only for informational and nonprofit purposes; any unauthorized use of this software for industrial or profit-oriented activities is expressly prohibited; and
(ii) any reproduction or modification retains all original notices including proprietary or copyright notices; and
(iii) reference to the original authors is given whenever results, which arise from the use of this software or any modification of it, are made public.
No other use of the materials and of any information incorporated thereto is hereby authorized.
In addition, be informed that some names are protected by trademarks which are the property of GRIP-UNINA, its researchers and/or other third parties whether a specific mention in that respect is made or not.
Disclaimers
This software is provided 'as-is', without any express or implied warranty.
In no event will the authors be held liable for any damages arising from the use of this software.
Transmission of user information
Any and all information or request for information you may direct to GRIP
through e-mail as may be linked to website http://www.grip.unina.it/
================================================
FILE: README.md
================================================
# Noiseprint: a CNN-based camera model fingerprint
[Noiseprint](https://ieeexplore.ieee.org/document/8713484) is a CNN-based camera model fingerprint
extracted by a fully Convolutional Neural Network (CNN).
## License
Copyright (c) 2019 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
All rights reserved.
This software should be used, reproduced and modified only for informational and nonprofit purposes.
By downloading and/or using any of these files, you implicitly agree to all the
terms of the license, as specified in the document LICENSE.txt
(included in this package)
## Installation
The code requires Python 3.x and Tensorflow 1.2.1 .
To install Python 3.x for Ubuntu, you can run:
```
apt-get update
apt-get install -y python3.5 python3.5-dev python3-pip python3-venv
```
We recommend to use a virtual environment:
```
python3.5 -m venv ../venv
source ../venv/bin/activate
pip install --upgrade pip
```
### Installation with GPU
Install Cuda8 and Cudnn5, more informetion on sites:
- https://developer.nvidia.com/cuda-downloads
- https://developer.nvidia.com/cudnn
Then install the requested libraries using:
```
cat noiseprint/requirements-gpu.txt | xargs -n 1 -L 1 pip install
```
### Installation without GPU
Install the requested libraries using:
```
cat noiseprint/requirements-cpu.txt | xargs -n 1 -L 1 pip install
```
## Usage
To extract the noiseprint, run:
```
python main_extraction.py