Improving-Deep-Watermarking-Robustness-with-an-Ensemble-of-Expert-Noisers

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Improving-Deep-Watermarking-Robustness-with-an-Ensemble-of-Expert-Noisers

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Distortion Agnostic watermarking based on DCT-Based Transformer

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# Improving Deep Watermarking Robustness with an Ensemble of Expert Noisers

## Result On Paper
https://docs.google.com/spreadsheets/d/1iB9iQ7p68j7MldXvhn7xEjoj2ajJLXeJtPSKtjqXa_I/edit?usp=sharing

<p align="center">
<img src="docs/Watermarked_Overall.jpg" width="800px"/>
<br>
The overall architecture of our DCT-Transformer and Watermarking System
</p>

## Single-GPU Command
```
train from scratch (not pretraining) ---> python train.py  --batch_size 32  --dataset /coco --pretrain_iter 0
train from scratch (pretraining)     ---> python train.py  --batch_size 32 --dataset /coco --pretrain_iter 5000
load pretrain checkpoint             ---> python train.py  --batch_size 32 --resume_pretrain /checkpoint/pretrain500.pyt
load train checkpoint                ---> python train.py  --batch_size 32 --resume /checkpoint/train500.pyt --> auto skip necst pretraining
```

## Multi-GPU Command
```
CUDA_VISIBLE_DEVICES=[rank of GPU] python -m torch.distributed.launch --nproc_per_node=[GPU size] torch_ddp_train.py --dataset /coco
```

## Dataset
COCO Dataset please refer to https://cocodataset.org/#home

## Citation
```
https://github.com/ando-khachatryan/HiDDeN
```

Improving-Deep-Watermarking-Robustness-with-an-Ensemble-of-Expert-Noisers

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