Exposing low-quality deepfake videos of Social Network Service using Spatial Restored Detection Framework
Yükleniyor...
Dosyalar
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The increasing abuse of facial manipulation methods, such as FaceSwap, Deepfakes etc., seriously threatens the authenticity of digital images/videos on the Internet. Therefore, it is of great importance to identify the facial videos to confirm the contents and avoid fake news or rumors. Many researchers have paid great attention to the detection of deepfakes and put forward a number of deep-learning-based detection models. The existing approaches mostly face the performance degradation in detecting low-quality(LQ) videos, i.e. heavily compressed or low-resolution videos through some SNS (Social Network Service), resulting in the limitation in real applications. To address this issue, in this paper, a novel Spatial Restore Detection Framework(SRDF) is proposed for improving the detection performance for LQ videos by restoring spatial features. We designed a feature extraction-enhancement block and a mapping block inspired by super-resolution methods, to restore and enhance texture features. An attention module was introduced to guide the texture features restoration and enhancement stage attending to different local areas and restoring the texture features. Besides, an improved isolated loss was put forward to prevent the expansion of a single area concerned. Moreover, we adopted a regional data augmentation strategy to prompt feature restore and enhancement in the region attended. Extensive experiments conducted on two deepfake datasets have validated the superiority of the proposed method compared to the state-of-the-art, especially in the scenarios of detecting low-quality deepfake videos.
Açıklama
This work was supported by the National Natural Science Foun-dation of China (62172165) ; the Science and Technology Program of Guangzhou (202102020582) ; the Natural Science Foundation of Guangdong Province (2022A1515010325) ; the Guangzhou Basic and Applied Basic Research Project (202201010742) .
Anahtar Kelimeler
Deepfake Detection, Video Forensics, Super Resolution, Attention Mechanism, Face, Images
Kaynak
Expert Systems with Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
231
Sayı
Künye
Li, Y., Bian, S., Wang, C., Polat, K., Alhudhaif, A., & Alenezi, F. (2023). Exposing low-quality deepfake videos of Social Network Service using Spatial Restored Detection Framework. Expert Systems with Applications, 120646.
