Face forgery detection plays an important role in personal privacy and social security. With the development of adversarial generative models, high-quality forgery images become more and more indistinguishable from real to humans. Existing methods always regard as forgery detection task as the common binary or multi-label classification, and ignore exploring diverse multi-modality forgery image types, e.g. visible light spectrum and near-infrared scenarios. In this paper, we propose a novel Hierarchical Forgery Classifier for Multi-modality Face Forgery Detection (HFC-MFFD), which could effectively learn robust patches-based hybrid domain representation to enhance forgery authentication in multiple-modality scenarios. The local spatial hybrid domain feature module is designed to explore strong discriminative forgery clues both in the image and frequency domain in local distinct face regions. Furthermore, the specific hierarchical face forgery classifier is proposed to alleviate the class imbalance problem and further boost detection performance. Experimental results on representative multi-modality face forgery datasets demonstrate the superior performance of the proposed HFC-MFFD compared with state-of-the-art algorithms. The source code and models are publicly available at https://github.com/EdWhites/HFC-MFFD.

翻译：人脸伪造检测在个人隐私与社会安全中扮演着重要角色。随着对抗生成模型的发展，高质量伪造图像愈发难以被人类与真实图像区分。现有方法通常将伪造检测任务视为常规二分类或多标签分类，忽视了对多样化多模态伪造图像类型（如可见光谱与近红外场景）的探索。本文提出一种新颖的层次化伪造分类器用于多模态人脸伪造检测（HFC-MFFD），该分类器可有效学习基于鲁棒图像块的混合域表示，从而增强多模态场景下的伪造鉴别能力。局部空间混合域特征模块旨在探索图像域与频域中局部显著人脸区域的强判别性伪造线索。此外，所提出的特定层次化人脸伪造分类器可缓解类别不平衡问题并进一步提升检测性能。在代表性多模态人脸伪造数据集上的实验结果表明，所提出的HFC-MFFD相较于现有最优算法具有更优越的性能。源代码与模型已公开于https://github.com/EdWhites/HFC-MFFD。