HiDDeN

Pytorch implementation of paper "HiDDeN: Hiding Data With Deep Networks" by Jiren Zhu*, Russell Kaplan*, Justin Johnson, and Li Fei-Fei: https://arxiv.org/abs/1807.09937
*: These authors contributed equally

The authors have Lua+Torch implementation here: https://github.com/jirenz/HiDDeN

I have tested this on Pytorch 1.0. Note that this is a work in progress, and I was not yet able to fully reproduce the results of the original paper.

Requirements

You need Pytorch 1.0 with TorchVision to run this, and matplotlib. If you want to use Tensorboard, you need to install TensorboardX and Tensorboard. This allows to use a subset of Tensorboard functionality to visualize the training. However, this is optional. The code has been tested and runs on Ubuntu 16.04 and Windows 10.

Data

We use 10,000 images for training and 1,000 images for validation. Following the original paper, we chose those 10,000 + 1,000 images randomly from one of the coco datasets. http://cocodataset.org/#download

The data directory has the following structure:

<data_root>/
  train/
    train_class/
      train_image1.jpg
      train_image2.jpg
      ...
  val/
    val_class/
      val_image1.jpg
      val_image2.jpg
      ...

train_class and val_class folders are so that we can use the standard torchvision data loaders without change.

Running

You will need to install the dependencies, then run

python main.py --data-dir <data_root> --batch-size <b>

By default, tensorboard logging is enabled. To use this, you need to install Tensorboard and TensorboardX. However, if you don't want to use Tensorboard, you don't need to install Tensorboard/TensorboardX. Simply run with the --no-tensorboard switch.

There are additional parameters for main.py, see the code. I will add their description here over time.

Each run creates a folder in ./runs/ and stores all the info about the run in there.

Experiments

I will post experimental runs in here.