After that, we adopt a consensus process to predict a deterministic result based on a set of samples from the distribution.
Then, we design a two-level perturbation fusion strategy to alleviate the conflict between the adversarial watermarks generated by different facial images and models.
Firstly, we propose a patch selection and refining scheme to find the pixels which have the greatest importance for attack and remove the inconsequential perturbations gradually.
Our proposed network is a single model approach that can be trained for handling a wide range of quality factors while consistently delivering superior or comparable image artifacts removal performance.
Additionally, the pyramid non-local block can be directly incorporated into convolution neural networks for other image restoration tasks.