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Data-Driven Deepfake Forensics Model Based on Large-Scale Frequency and Noise Features
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AI
DOI：10.1109/MIS.2022.3217391.png)
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Abstract
En 中文
With the rapid development of deep learning and communication technology, the application of streaming media services and social software have gone deep into life. However, in the face of many uncertain factors in data dissemination, protecting privacy and security is particularly important. In order to solve the abovementioned problems, this study proposes a deep face forgery forensics method with frequency domain and noise features. In this method, discrete cosine transform is proposed to perceive the forgery trace features of different frequency bands in the frequency domain. At the same time, the spatial rich model is used for guidance to enhance the traces of forged noise. Then, large-scale network and single center loss function are introduced to improve the forensics ability of the model. Experimental results on several databases such as faceforensics++, celeb DF, and DFDC show that this method can effectively improve the accuracy of forensics.
Keywords:
Feature extraction
Forgery
Faces
Training
Frequency-domain analysis
Convolution
Face recognition