cxmscb / PIMoG-An-Effective-Screen-shooting-Noise-Layer-Simulation-for-Deep-Learning-Based-Watermarking-Netw

This is the code for paper: ``PIMoG : An Effective Screen-shooting Noise-Layer Simulation for Deep-Learning-Based Watermarking Network. .Fang, Han, et al. Proceedings of the 30th ACM International Conference on Multimedia. 2022.

MM22_PIMoG

This is the code for paper: ``PIMoG : An Effective Screen-shooting Noise-Layer Simulation for Deep-Learning-Based Watermarking Network. .Fang, Han, et al. Proceedings of the 30th ACM International Conference on Multimedia. 2022.

To train the network

python main.py --dataset train_mask --mode train_mask --image_dir '.../' --image_val_dir '.../'

PS: Before training, pls generate the training dataset like the examples shown in the 'Datasets/COCOMask/train/train_class/'

To use the pre-trained model to embed watermark

python main.py --dataset test_embedding --mode test_embedding --image_dir '.../' --embedding_epoch 99 --distortion ScreenShooting

To test the accuracy of the network

python main.py --dataset test_accuracy --mode test_accuracy --image_dir '.../' --image_val_dir '.../' --embedding_epoch 99 --distortion ScreenShooting

PS: After screen shooting process, please first utilize the ``PespectiveTransformation.m'' to make a perspective correction. Besides, when capturing the watermarked image, enlarge it and make it occupying at least 1 / 4 of the screen for better performance.

Example:

python main.py --dataset test_embedding --mode test_embedding --image_dir 'Datasets/images/' --embedding_epoch 99 --distortion ScreenShooting

python main.py --dataset test_accuracy --mode test_accuracy --image_dir 'Datasets/Recover/capture/' --image_val_dir 'Datasets/Recover/capture/' --embedding_epoch 99 --distortion ScreenShooting