Artifacts-Disentangled Adversarial Learning for Deepfake Detection
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DOI: 10.1109/tcsvt.2022.3217950
Abstract: Due to the development of facial manipulation technologies, the generated deepfake videos cause a severe trust crisis in society. Existing methods prove that effective extraction of the artifacts introduced during the forgery process is essential… read more here.
Keywords: artifacts disentangled; detection; adversarial learning; disentangled adversarial ... See more keywords
Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach
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DOI: 10.1109/tcsvt.2025.3572508
Abstract: In recent years, the multimedia forensics and security community has seen remarkable progress in multitask learning for DeepFake (i.e., face forgery) detection. The prevailing approach has been to frame DeepFake detection as a binary classification… read more here.
Keywords: semantics; detection; multitask learning; face ... See more keywords
Masked Relation Learning for DeepFake Detection
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2023.3249566
Abstract: DeepFake detection aims to differentiate falsified faces from real ones. Most approaches formulate it as a binary classification problem by solely mining the local artifacts and inconsistencies of face forgery, which neglect the relation across… read more here.
Keywords: masked relation; relation learning; learning deepfake; deepfake detection ... See more keywords