This repository is created to assist people encountering difficulties running the offical repository from DAI-Lab, as the official one has not received updates for the past several years. Additionally, I have optimized the official version to enhance execution speed while maintaining overall performance integrity. Lastly, I have conducted testing on Windows 11 using the latest Python 3.11 and PyTorch 2.0.1

1. Install PyTorch, Numpy, OpenCV, Pandas, ArgParse
2. Install GitBash from https://git-scm.com/
3. Install wget from https://gnuwin32.sourceforge.net/packages/wget.htm
4. Install Torch DCT using pip install torch_dct

1. Clone this repository

2. Open GitBash Terminal and Download Hollywood2 Training Dataset
   Acquiring the dataset may require several hours, depending upon the speed of your internet connection

   cd data
   bash download.sh
   

3. Train RivaGAN Model
   The hyperparameter settings align with the official specifications and are currently configured to their default values

   python train.py 
   python train.py --epochs 200 --lr 0.001 --data_dim 64 
   

   Default Hyperparameters Details:

   • --epochs: 300
   • --train_batch: 12
   • --lr: 0.0005
   • --num_workers: 16
   • --data_dim: 32
   • --use_critic: True
   • --use_adversary: True
   • --use_noise: True
   • --use_bit_inverse: True

4. Inference RivaGAN Model
   After completing the model training, our objective is to encode a data watermark onto a video and subsequently extract it from the encoded footage. After the inference process, it will generate output_log.txt file, providing a detailed record of the extracted data from each frame in the video and a watermarked video that contains the data

   python inference.py --model_weight your_weight_path/model.pt
   python inference.py --model_weight your_weight_path/model.pt --random_data No --your_data "1100 1001 0011 0000 1111 0101 1100 0011" --fps 30
   

   Default Hyperparameters Details:

   • --data_dim: 32
     • The data dimensions must correspond with the dimensions used during the model training
   • --model_weight: None
     • Must be added
   • --random_data: Yes
   • --your_data: None
     • Set --random_data to No to use your own data
   • --video_location: ./data/hollywood2/val/actioncliptest00002.avi
   • --fps: 25
     • Watermaked video output FPS

• make_pair function has been reverted to its original code due to instability issues during training

• Incorporated pre-trained RivaGAN model, which was trained using 32-bit data dimensions

• Enhanced code optimization for encoding and decoding processes (3.5 ~ 4x speed increase)