/F3Net

Unofficial implementation of ECCV20 paper "Thinking in frequency: Face forgery detection by mining frequency-aware clues"

Primary LanguagePython

Thinking in frequency: Face forgery detection by mining frequency-aware clues

GitHub top languageGitHub last commitGitHub repo size
European Conference on Computer Vision 2020

This implementation is mainly based on the desciption in the paper on top of XceptionNet. The inplementation has three models:

  • MixBlock: Two-stream Collaborative Framework (@ block 7 and block 12). If you change the input image size, revise the width and height parameters in lines 49 and 50.
  • FAD: Frequency-aware Decomposition
  • LFS: Local Frequency Statistics

Source code is mainly referenced from this repo.
Download pre-trained XceptionNet from here.

Star if you find it useful. ⭐