结合深度伪造特征对比的人脸伪造检测

doi:10.16451/j.cnki.issn1003-6059.202409003

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Abstract With the continuous development of artificial intelligence-generated content technology, the diversity of forgery techniques presents significant challenges to existing detection methods. Most current detection methods are based on facial forgery features extracted by different advanced convolutional neural networks. However, these methods are trained on datasets containing known forgery techniques, and their generalization capabilities are inadequate to handle images forged by unknown methods. Therefore, a face forgery detection method combined with deep forgery features comparison is proposed, and it exhibits excellent adaptability to unknown forgery techniques. The proposed approach consists of two stages. First, similar features of different forgery techniques are explored, and a meta-learning-based similar feature fusion network is introduced. This network leverages the learning capabilities of meta-learning to capture the similar features among different forgery methods. Second, unique forgery features specific to individual task are taken into account, and a task-specific uniqueness fine-tuning method is proposed to enhance the adaptability of the model to unknown forgery techniques.Cross-manipulation testing demonstrates that the proposed method improves the performance with superior detection capability against attacks from unknown forgery techniques.

Key words： Face Forgery Detection Deep Fake Meta-Learning Similar Feature Fusion Forgery Feature Mining

Received: 26 June 2024

ZTFLH:

TP 391

Fund:General Project of National Natural Science Foundation of China(No.62272141)

Corresponding Authors: GAO Xinjian, Ph.D., associate professor. His research interests include image processing, deep learning, artificial intelligence and machine learning.

About author:: GAO Jun, Ph.D., professor. His research interests include image processing, pattern recognition, neural network theory and applications, optoelectronic information processing and intelligent information proce-ssing. DA Zikai, Ph.D. candidate. His research interests include image processing, deep lear-ning, artificial intelligence and machine lear-ning. LI Zhaowei, Master student. His research interests include face forgery detection, computer vision and graphic processing.

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Cite this article:

LI Zhaowei,GAO Xinjian,DA Zikai等. Face Forgery Detection Combined with Deep Forgery Features Comparison[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(9): 786-797.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202409003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2024/V37/I9/786

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