@inproceedings{7ee4bda5cb6b48059a43ab8ae38efa9d,
title = "Forensics Forest: Multi-scale Hierarchical Cascade Forest for Detecting GAN-generated Faces",
abstract = "We describe a simple and effective method called ForensicsForest to detect GAN-generate faces. Instead of using the commonly used CNN models, we describe a novel multi-scale hierarchical cascade forest, which takes semantic and frequency features as input, and hierarchically cascades different levels of features for authenticity prediction. We then propose a multi-scale ensemble, which comprehensively considers different levels of information to improve the performance further. Our method is validated on state-of-the-art GAN-generated face datasets in comparison with several CNN models, which demonstrates the surprising effectiveness of our method in detecting GAN-generated faces.",
keywords = "Digital forensics, GAN-generated faces detection, Random forest",
author = "Jiucui Lu and Yuezun Li and Jiaran Zhou and Bin Li and Siwei Lyu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Multimedia and Expo, ICME 2023 ; Conference date: 10-07-2023 Through 14-07-2023",
year = "2023",
doi = "10.1109/ICME55011.2023.00394",
language = "English",
series = "Proceedings - IEEE International Conference on Multimedia and Expo",
publisher = "IEEE Computer Society",
pages = "2309--2314",
booktitle = "Proceedings - 2023 IEEE International Conference on Multimedia and Expo, ICME 2023",
address = "United States",
}