ForensicsForest Family: A Series of Multi-scale Hierarchical Cascade
  Forests for Detecting GAN-generated Faces
Jiucui Lu Yuezun Li Jiaran Zhou Bin Li Junyu Dong Siwei Lyu
The prominent progress in generative models has significantly improved the
reality of generated faces, bringing serious concerns to society. Since recent
GAN-generated faces are in high realism, the forgery traces have become more
imperceptible, increasing the forensics challenge. To combat GAN-generated
faces, many countermeasures based on Convolutional Neural Networks (CNNs) have
been spawned due to their strong learning ability. In this paper, we rethink
this problem and explore a new approach based on forest models instead of CNNs.
Specifically, we describe a simple and effective forest-based method set called
{\em ForensicsForest Family} to detect GAN-generate faces. The proposed
ForensicsForest family is composed of three variants, which are {\em
ForensicsForest}, {\em Hybrid ForensicsForest} and {\em Divide-and-Conquer
ForensicsForest} respectively. ForenscisForest is a newly proposed Multi-scale
Hierarchical Cascade Forest, which takes semantic, frequency and biology
features as input, hierarchically cascades different levels of features for
authenticity prediction, and then employs a multi-scale ensemble scheme that
can comprehensively consider different levels of information to improve the
performance further. Based on ForensicsForest, we develop Hybrid
ForensicsForest, an extended version that integrates the CNN layers into
models, to further refine the effectiveness of augmented features. Moreover, to
reduce the memory cost in training, we propose Divide-and-Conquer
ForensicsForest, which can construct a forest model using only a portion of
training samplings. In the training stage, we train several candidate forest
models using the subsets of training samples. Then a ForensicsForest is
assembled by picking the suitable components from these candidate forest
models...