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...

翻译：生成模型的显著进步极大地提升了生成人脸的真实性，给社会带来了严重关切。由于近期GAN生成的人脸具有高度逼真性，其伪造痕迹变得更加难以察觉，增加了法医鉴定的挑战性。为对抗GAN生成人脸，许多基于卷积神经网络（CNN）的方法因其强大的学习能力应运而生。本文重新审视这一问题，并探索一种基于森林模型而非CNN的新方法。具体而言，我们描述了一套简单有效的基于森林的方法集——"法医森林家族"，用于检测GAN生成人脸。所提出的法医森林家族由三种变体组成，分别为"法医森林"、"混合法医森林"和"分治法医森林"。法医森林是一种新提出的多尺度层次级联森林，它以语义、频率和生物学特征为输入，层次化级联不同层次的特征进行真实性预测，进而采用多尺度集成方案综合考量不同层次信息以进一步提升性能。在法医森林基础上，我们开发了混合法医森林——一种集成CNN层的扩展版本，以进一步优化增强特征的有效性。此外，为降低训练中的内存开销，我们提出分治法医森林，该方法仅需使用部分训练样本即可构建森林模型。在训练阶段，我们使用训练样本子集训练多个候选森林模型，然后通过从这些候选模型中选择合适组件来组装法医森林...