Deepfake Algorithm Using Multiple Noise Modalities with Two-Branch Prediction Network
Journal
2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
Pages
1662-1669
Date Issued
2021
Author(s)
Hsu H.-W
Abstract
In this paper, we propose a facial manipulation detection method based on multiple image noise analysis modalities and a two-branch prediction network to separation different types of forgery artifacts. The proposed architecture reveals whether the input image can be decomposed into a blending of two images from different sources, and checks whether some patches of the input image are generated from a deep learning networks at the same time. We observe that most of the existing forgery detection work] only focuses on finding one of the blending or manipulation artifacts in the input image. As a result, this method provides an effective way for forgery detection by simultaneously checking the manipulation and blending artifacts. In addition, for use with different types of image noise analysis modalities, our method can find more robust detection features in the high-frequency domain compared with traditionally detection in the RGB domain, thereby obtaining better performance. Extensive experiments show that our method outperforms other existing forgery detection methods on detecting synthesized face image, no matter on detecting training dataset or on detecting unseen face manipulation techniques. © 2021 APSIPA.
Other Subjects
Face recognition; Frequency domain analysis; Image analysis; Branch prediction; Detection features; Detection methods; Forgery detections; Image noise analysis; Input image; Learning network; Multiple image; Proposed architectures; Robust detection; Deep learning
Type
conference paper