DeepMindBreak

Decensoring Hentai with Deep Neural Networks

This project applies an implementation of Globally and Locally Consistent Image Completion to the problem of hentai decensorship. Using a deep fully convolutional neural network, DeepMindBreak can replace censored artwork in hentai with plausible reconstructions. The user needs to only specify the censored regions.

Limitations

This project is LIMITED in capability. It is a proof of concept of ongoing research.

The decensorship works ONLY with color hentai images that have minor bar censorship of the penis or vagina.

It does NOT work with:

• Black and white images
• Monochrome images
• Hentai containing screentones (e.g. printed hentai)
• Real life porn
• Mosaic censorship
• Censorship of nipples
• Censorship of anus
• Animated gifs/videos

In particular, if a vagina or penis is completely censored out, inpainting will be ineffective.

Dependencies

• Python 2
• TensorFlow 1.5
• Pillow
• tqdm
• matplotlib (only for running test.py)

Model

The pretrained model can be downloaded from https://drive.google.com/open?id=1mWHYSj0LDSbJQQxjR4hUMykQkVve2U3Q.

Unzip the contents into the /models/ folder.

Usage

I. Decensoring hentai

The decensorship process is fairly involved. A user interface will eventually be released to streamline the process.

Using image editing software like Photoshop or GIMP, paint the areas you want to decensor the color with RGB values of (0,255,0). For each censored region, crop 128 x 128 size images containing the censored regions from your images and save them as new ".png" images.

Move the cropped images to the "decensor_input_images" directory. Decensor the images by running

$ python decensor.py

Decensored images will be saved to the "decensor_output_images" directory. Paste the decensored images back into the original image.

II. Train the pretrained model on custom dataset

Put your custom dataset for training in the "data/images" directory and convert images to npy format.

$ cd training_data
$ python to_npy.py

Train pretrained model on your custom dataset.

$ python train.py

To do

• Add Python 3 compatibility
• Retrain for arbitrary shape censors
• Add a user interface
• Incorporate GAN loss into training

Contributions are welcome!

License

Example image by dannychoo under CC BY-NC-SA 2.0 License. The example image is modified from the original, which can be found here.

Model is licensed under CC BY-NC 3.0 License.

Code is licensed under MIT License and is modified from tadax's project Globally and Locally Consistent Image Completion with TensorFlow , which is an implementation of the paper Globally and Locally Consistent Image Completion. It also has a modified version of parosky's project poissonblending.

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Copyright (c) 2018 tadax, parosky, deeppomf

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.