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 employs a multi-scale ensemble scheme to consider different levels of information comprehensively for further performance improvement. Building upon ForensicsForest, we create Hybrid ForensicsForest, an extended version that integrates the CNN layers into models, to further enhance the efficacy of augmented features. Furthermore, to reduce memory usage during training, we introduce 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 selecting suitable components from these candidate forest models. Our method is validated on state-of-the-art GAN-generated face datasets and compared with several CNN models, demonstrating the surprising effectiveness of our method in detecting GAN-generated faces.

中文翻译：

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ForensicsForest 系列：一系列多尺度分层级联森林，用于检测 GAN 生成的人脸

生成模型的显着进步显着提高了生成人脸的真实性，引起了社会的严重关注。为了对抗 GAN 生成的人脸，许多基于卷积神经网络（CNN）的对策因其强大的学习能力而应运而生。在本文中，我们重新思考这个问题，并探索一种基于森林模型而不是 CNN 的新方法。具体来说，我们描述了一种简单而有效的基于森林的方法集，称为 ForensicsForest Family，用于检测 GAN 生成的人脸。 ForensicsForest 家族由三个变体组成：ForensicsForest、Hybrid ForensicsForest 和 Divide-and-Conquer ForensicsForest。 ForenscisForest是一种新颖的多尺度分层级联森林，以外观、频率和生物特征为输入，分层级联不同级别的特征进行真实性预测，并采用多尺度集成方案综合考虑不同级别的信息以进一步提高性能改进。在 ForensicsForest 的基础上，我们创建了 Hybrid ForensicsForest，这是一个将 CNN 层集成到模型中的扩展版本，以进一步增强增强功能的功效。此外，为了减少训练期间的内存使用，我们引入了分而治之的 ForensicsForest，它可以仅使用一部分训练样本构建森林模型。在训练阶段，我们使用训练样本的子集训练几个候选森林模型。然后，通过从这些候选森林模型中选择合适的组件来组装 ForensicsForest。我们的方法在最先进的 GAN 生成的人脸数据集上进行了验证，并与多个 CNN 模型进行了比较，证明了我们的方法在检测 GAN 生成的人脸方面令人惊讶的有效性。