Cnn Watermark Removal Save

Fully convolutional deep neural network to remove transparent overlays from images

Project README

Warning! The architecture used in this project does not generalize well. You may want to check This inpainting technique will likely give you better results.

Fully convolutional watermark removal attack

Deep learning architecture to remove transparent overlays from images.


Top: left is with watermark, middle is reconstruction and right is the mask the algo predicts (the neural net was never trained using text or this image)

Bottom: Pascal dataset image reconstructions. When the watermarked area is saturated, the reconstruction tends to produce a gray color.

Design choices

At train time, I generate a mask. It is a rectangle with randomly generated parameters (height, width, opacity, black/white, rotation). The mask is applied to a picture and the network is trained to find what was added. The loss is abs(prediction, image_perturbations)**1/2. It is not on the entire picture. An area around the mask is used to make the problem more tractable.

The network architecture does not down-sample the image. The prediction with a down-sampling network were not accurate enough. To have a large enough receptive field and not blow up the compute, I use dilated convolution. So concretely, I have a densenet style block, a bunch of dilated convolutions and final convolution to output a picture (3 channels). I did not spend much time doing hyper-parameters optimization. There's room to get better results using the current architecture.

Limitations: this architectures does not generalize to watermarks that are too different from the one generated with create_mask and it produces decent results only when the overlay is applied in an additive fashion.


This project uses Tensorflow. Install packages withpip install -r requirements.txt

You will need the jpeg library to compile Pillow from source: sudo apt-get install libjpeg-dev zlib1g-dev

You will also need to download the pascal dataset (used by default) from or CIFAR10 python version from (use flag --dataset=dataset_cifar). Make sure the extract the pascal dataset under a directory called data. The project directory should then have the directory cifar-10-batches-py and/or data/VOCdevkit/VOC2012/JPEGImages. If you want to use your own images, place them in data/VOCdevkit/VOC2012/JPEGImages/.

To train the network python3 --logdir=save/. It starts to produce some interesting results after 12000 steps.

To use the network for inference, you can run python --image assets/cat.png --selection assets/cat-selection.png this will create a new image output.png.

Pretrained weights

Here you can find the weights: put them in /tmp/

Open Source Agenda is not affiliated with "Cnn Watermark Removal" Project. README Source: marcbelmont/cnn-watermark-removal
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