Domain Generalization for Face Forgery Detection by Style Transfer

Although deep fake detection models have made significant progress, the challenge of performance degradation remains yet for unseen datasets. To address this, we introduce a novel data generalization approach using style transfer to generate images in various domains. Utilizing style transfer, we cr...

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Bibliographic Details
Main Authors: Kim, Taehoon, Choi, Jongwook, Cho, Hyunjin, Lim, HyoungJun, Choi, Jongwon
Format: Conference Proceeding
Language:English
Subjects:
Consumer electronics
data augmentation
Deepfake detection
Deepfakes
Degradation
Detectors
Faces
Forgery
forgery detection
style transfer
Training
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Summary:Although deep fake detection models have made significant progress, the challenge of performance degradation remains yet for unseen datasets. To address this, we introduce a novel data generalization approach using style transfer to generate images in various domains. Utilizing style transfer, we create a new domain where domain-specific information is eliminated and subsequently train our model on the new domain. Our approach enhances the generalization performance of the detector by adding the style-transferred images to train the deepfake detector. Through the experiments, we confirm that the performance on the trained dataset remains unchanged while achieving an improvement of 8.8% on an unseen dataset. Therefore, We verify the effectiveness of the style-transferred images for generalizing the performance upon unseen datasets.
ISSN:2158-4001
DOI:10.1109/ICCE59016.2024.10444215