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Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-Level Telltales

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Autor(es):
Kong, Chenqi ; Chen, Baoliang ; Li, Haoliang ; Wang, Shiqi ; Rocha, Anderson ; Kwong, Sam
Número total de Autores: 6
Tipo de documento: Artigo Científico
Fonte: IEEE Transactions on Information Forensics and Security; v. 17, p. 16-pg., 2022-01-01.
Resumo

The technological advancements of deep learning have enabled sophisticated face manipulation schemes, raising severe trust issues and security concerns in modern society. Generally speaking, detecting manipulated faces and locating the potentially altered regions are challenging tasks. Herein, we propose a conceptually simple but effective method to efficiently detect forged faces in an image while simultaneously locating the manipulated regions. The proposed scheme relies on a segmentation map that delivers meaningful high-level semantic information clues about the image. Furthermore, a noise map is estimated, playing a complementary role in capturing low-level clues and subsequently empowering decision-making. Finally, the features from these two modules are combined to distinguish fake faces. Extensive experiments show that the proposed model achieves state-of-the-art detection accuracy and remarkable localization performance. (AU)

Processo FAPESP: 17/12646-3 - Déjà vu: coerência temporal, espacial e de caracterização de dados heterogêneos para análise e interpretação de integridade
Beneficiário:Anderson de Rezende Rocha
Modalidade de apoio: Auxílio à Pesquisa - Temático