主管：中华人民共和国司法部
主办：司法鉴定科学研究院
ISSN 1671-2072  CN 31-1863/N
中国司法鉴定

中国司法鉴定 ›› 2021 ›› Issue (4): 72-76.DOI: 10.3969/j.issn.1671-2072.2021.04.009

• 鉴定科学 • 上一篇    下一篇

基于迁移学习的AI合成人脸图像鉴别研究

牛瑾琳，王华朋，张琨瑶，倪令格，刘元周   

  1. 中国刑事警察学院 公安信息技术与情报学院，辽宁 沈阳 110035

  • 收稿日期:2020-11-09 出版日期:2021-07-15 发布日期:2021-07-25
  • 作者简介:牛瑾林（1997-），女，硕士研究生，主要从事音频图像的证据处理以及深度学习。E-mail:610019627@qq.com

Research on AI Synthetic Face Image Identification Based on Transfer Learning

NIU Jinlin, WANG Huapeng, ZHANG Kunyao, NI Lingge, LIU Yuanzhou   

  1. School of Police Information Science and Technology, Criminal Investigation Police University of China, Shenyang 110035, China

  • Received:2020-11-09 Published:2021-07-15 Online:2021-07-25

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摘要：

目的 人工智能（Artificial Intelligence，AI）生成高质量人脸图像的伪造技术愈发成熟，使得人脸图像的真实性检验面临重大考验。利用一种深度学习的方法对真伪人脸图像进行二分类，以实现对伪造图像的识别。方法 提出一种基于迁移学习的方法，构建MobileNetV2网络，保留其在ImageNet数据集上的预训练权值，并对采用FaceSwap技术生成的5 274张假脸图像和6 650张真脸图像进行辨识。结果 迁移模型在测试集上预测的准确度能达到0.94，该网络架构对于真假人脸图像的辨别具有一定的稳健性。结论 利用迁移学习的方法能够实现对真伪人脸图像的辨识，在一定程度上对AI合成人脸图像的真实性检验具有借鉴意义。

关键词:

MobileNetV2网络, FaceSwap技术, AI合成人脸图像辨别

Abstract:

Objective High-quality face image forgery generated by Artificial Intelligence （AI） technology has become more and more mature, which makes the authenticity testing of face images have a great challenge. This article uses the deep learning approach to classify real and fake face images in order to achieve the identification of fake images. Methods In this paper, we propose a method of transfer learning that we construct MobileNetV2 network and retain its pre-training weight on ImageNet data set. We use 5 274 fake face images produced by FaceSwap technology and 6 650 real face images for identification. Results The migration model gets an accuracy of 0.94 in the test set and has a certain stability to identify real and fake face images. Conclusion Using transfer learning approach can achieve the identification of real and fake face images. To some extent, this method can be used for reference in authenticity testing about AI synthetic face images.

Key words:

MobileNetV2 network, FaceSwap technology, AI synthetic face image identification

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