ForgeryNIR: Deep Face Forgery and Detection in Near-Infrared Scenario

计算机科学 面子(社会学概念) 模态(人机交互) 人工智能 人脸检测 面部识别系统 模式识别(心理学) 计算机视觉 深度学习 社会科学 社会学
作者
Yukai WANG,Chunlei Peng,Decheng Liu,Nannan Wang,Xinbo Gao
出处
期刊:IEEE Transactions on Information Forensics and Security [Institute of Electrical and Electronics Engineers]
卷期号:17: 500-515 被引量:29
标识
DOI:10.1109/tifs.2022.3146766
摘要

Deep face forgery and detection is an emerging topic due to the development of GANs. Face forgery detection relies greatly on existing databases for evaluation and adequate training examples for data-hungry machine learning algorithms. However, considering the wide application of face recognition in near-infrared scenarios, there is no publicly available face forgery database that includes near-infrared modality currently. In this paper, we present an attempt at constructing a large-scale dataset for face forgery detection in the near-infrared modality and propose a new forgery detection method based on knowledge distillation named cross-modality knowledge distillation aiming to use a teacher model which is pre-trained on the visible light-based (VIS) big data to guide the student model with a small amount of near-infrared (NIR) data. The proposed near-infrared face forgery dataset, named ForgeryNIR, contains a total of over 50,000 real and fake identities. A number of perturbations are applied to help simulate real-world scenarios. All source images in ForgeryNIR are collected from CASIA NIR-VIS 2.0, and fake images are generated via multiple GAN techniques. The proposed dataset fills the gap of face forgery detection research in the near-infrared modality. A comprehensive study on six representative detection baselines is conducted to evaluate the performance of face forgery detection algorithms in the NIR domain. We further construct a hard testing set, named ForgeryNIR+, which contains forged images that have bypassed existing face forgery detection methods. The proposed datasets will be publicly available and aim to help boost further research on face forgery detection, as well as NIR face detection and recognition.
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