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The prominent progress in generative models has significantly improved the authenticity of generated faces, raising serious concerns in society. To combat GAN-generated faces, many countermeasures based on Convolutional Neural Networks (CNNs) have been spawned due to their strong learning capabilities. In this paper, we rethink this problem and explore a new approach based on forest models instead of CNNs. Concretely, we describe a simple and effective forest-based method set, termed ForensicsForest Family, to detect GAN-generate faces. The ForensicsForest family is composed of three variants: ForensicsForest, Hybrid ForensicsForest and Divide-and-Conquer ForensicsForest. ForenscisForest is a novel Multi-scale Hierarchical Cascade Forest that takes appearance, frequency, and biological features as input, hierarchically cascades different levels of features for authenticity prediction, and …