{
    "info": {
        "author": "MIT Data To AI Lab",
        "author_email": "dailabmit@gmail.com",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
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        "description": "<p align=\"left\">\n<img width=15% src=\"https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png\" alt=\u201cSteganoGAN\u201d />\n<i>An open source project from Data to AI Lab at MIT.</i>\n</p>\n\n[![PyPI Shield](https://img.shields.io/pypi/v/steganogan.svg)](https://pypi.python.org/pypi/steganogan)\n[![Travis CI Shield](https://travis-ci.org/DAI-Lab/SteganoGAN.svg?branch=master)](https://travis-ci.org/DAI-Lab/SteganoGAN)\n\n# SteganoGAN\n\n- License: MIT\n- Documentation: https://DAI-Lab.github.io/SteganoGAN\n- Homepage: https://github.com/DAI-Lab/SteganoGAN\n\n## Overview\n\n**SteganoGAN** is a tool for creating steganographic images using adversarial training.\n\n## Installation\n\nTo get started with **SteganoGAN**, we recommend using `pip`:\n\n```bash\npip install steganogan\n```\n\nAlternatively, you can clone the repository and install it from source by running `make install`:\n\n```bash\ngit clone git@github.com:DAI-Lab/SteganoGAN.git\ncd SteganoGAN\nmake install\n```\n\nFor development, you can use the `make install-develop` command instead in order to install all\nthe required dependencies for testing and linting.\n\n## Usage\n### Command Line\n\n**SteganoGAN** includes a simple command line interface for encoding and decoding steganographic images.\n\n#### Hide a message inside an image\n\nTo create a steganographic image, you simply need to supply the path to the cover image and the secret \nmessage:\n\n```\nsteganogan encode [options] path/to/cover/image.png \"Message to hide\"\n```\n\n#### Read a message from an image\n\nTo recover the secret message from a steganographic image, you simply supply the path to the steganographic \nimage that was generated by a compatible model:\n\n```\nsteganogan decode [options] path/to/generated/image.png\n```\n\n#### Additional options\n\nThe script has some additional options to control its behavior:\n\n* `-o, --output PATH`: Path where the generated image will be stored. Defaults to `output.png`.\n* `-a, --architecture ARCH`: Architecture to use, basic or dense. Defaults to dense.\n* `-v, --verbose`: Be verbose.\n* `--cpu`: force CPU usage even if CUDA is available. This might be needed if there is a GPU\n  available in the system but the VRAM amount is too low.\n\n**WARNING**: Make sure to use the same architecture specification (`--architecture`) during both \nthe encoding and decoding stage; otherwise, `SteganoGAN` will fail to decode the message.\n\n### Python\n\nThe primary way to interact with **SteganoGAN** from Python is through the `steganogan.SteganoGAN` \nclass. This class can be instantiated using a pretrained model:\n\n```\n>>> from steganogan import SteganoGAN\n>>> steganogan = SteganoGAN.load('steganogan/pretrained/dense.steg')\nUsing CUDA device\n```\n\nOnce we have loaded our model, we can give it the input image path, the output image path, and \nthe secret message:\n\n```\n>>> steganogan.encode('research/input.png', 'research/output.png', 'This is a super secret message!')\nEncoding completed.\n```\n\nThis will generate an `output.png` image that closely resembles the input image but contains the \nsecret message. In order to recover the message, we can simply pass `output.png` to the `decode` \nmethod:\n\n```\n>>> steganogan.decode('research/output.png')\n'This is a super secret message!'\n```\n\n## Research\n\nWe provide example scripts in the `research` folder which demonstrate how you can train your own\n`SteganoGAN` models from scratch on arbitrary datasets. In addition, we provide a convenience \nscript in `research/data` for downloading two popular image datasets.\n\n## References\n\nIf you use SteganoGAN for your research, please consider citing the following work:\n\nZhang, Kevin Alex and Cuesta-Infante, Alfredo and Veeramachaneni, Kalyan. SteganoGAN: High Capacity Image Steganography with GANs. MIT EECS, January 2019. ([PDF](https://arxiv.org/pdf/1901.03892.pdf))\n\n```\n@article{zhang2019steganogan,\n  title={SteganoGAN: High Capacity Image Steganography with GANs},\n  author={Zhang, Kevin Alex and Cuesta-Infante, Alfredo and Veeramachaneni, Kalyan},\n  journal={arXiv preprint arXiv:1901.03892},\n  year={2019},\n  url={https://arxiv.org/abs/1901.03892}\n}\n```\n\n\n# History\n\n## 0.1.2\n\n* Add option to work with a custom pretrained model from CLI and Python\n* Refactorize Critics and Decoders to match Encoders code style\n* Make old pretrained models compatible with new code versions\n* Cleanup unneeded dependencies\n* Extensive unit testing\n\n## 0.1.1\n\n* Add better pretrained models.\n* Improve support for non CUDA devices.\n\n## 0.1.0 - First release to PyPi\n\n* SteganoGAN class which can be fitted, saved, loaded and used to encode and decode messages.\n* Basic command line interface that allow using pretrained models.\n* Basic and Dense pretrained models for demo purposes.\n\n\n",
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