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Cognitive Computation
Erschienen in:

02.06.2022

IR-Capsule: Two-Stream Network for Face Forgery Detection

verfasst von: Kaihan Lin, Weihong Han, Shudong Li, Zhaoquan Gu, Huimin Zhao, Jinchang Ren, Li Zhu, Jujian Lv

Erschienen in: Cognitive Computation | Ausgabe 1/2023

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Abstract

With the emergence of deep learning, generating forged images or videos has become much easier in recent years. Face forgery detection, as a way to detect forgery, is an important topic in digital media forensics. Despite previous works having made remarkable progress, the spatial relationships of each part of the face that has significant forgery clues are seldom explored. To overcome this shortcoming, a two-stream face forgery detection network that fuses Inception ResNet stream and capsule network stream (IR-Capsule) is proposed in this paper, which can learn both conventional facial features and hierarchical pose relationships and angle features between different parts of the face. Furthermore, part of the Inception ResNet V1 model pre-trained on the VGGFACE2 dataset is utilized as an initial feature extractor to reduce overfitting and training time, and a modified capsule loss is proposed for the IR-Capsule network. Experimental results on the challenging FaceForensics++ benchmark show that the proposed IR-Capsule improves accuracy by more than 3% compared with several state-of-the-art methods.
Vorheriger Artikel Deep, Flexible Data Embedding with Graph-Based Feature Propagation for Semi-supervised Classification
Nächster Artikel A Music Cognition–Guided Framework for Multi-pitch Estimation

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Metadaten
Titel
IR-Capsule: Two-Stream Network for Face Forgery Detection
verfasst von
Kaihan Lin
Weihong Han
Shudong Li
Zhaoquan Gu
Huimin Zhao
Jinchang Ren
Li Zhu
Jujian Lv
Publikationsdatum
02.06.2022
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 1/2023
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-022-10008-4

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