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• W4229011569 abstract "Training data may include personal information such as human faces, which requires anonymization to provide user privacy. However, after anonymization, the performance of the original machine learning (ML) model degrades due to the reduced or missing information. In this work, we introduce a novel privacy-preserving tensor decomposition (PTD) method to anonymize human faces. Further, we evaluate real vs. fake human face detection task as a practical use case scenario. Our approach achieves high performance as well as training data efficiency, where the essence of our approach is based on tensor decomposition to ensure face data privacy. In particular, we demonstrate that the core tensor of Tucker decomposition generated from the original face input can effectively represent the underlying characteristics of the original face data; that is, learning only from the core tensors is sufficient for differentiating real human face images from deepfakes. Also, we show that the original human face inputs are anonymized and cannot be recovered from the core tensors under different attacker models from the randomized HOOI algorithm. Through extensive experiments and analysis, we demonstrate that our method can result in high detection performance comparable to those of popular anonymization methods. Therefore, we show that our work strikes the balance between privacy and performance through the novel use of tensor decomposition." @default.
• W4229011569 created "2022-05-08" @default.
• W4229011569 creator A5003311970 @default.
• W4229011569 creator A5033106393 @default.
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• W4229011569 date "2022-04-25" @default.
• W4229011569 modified "2023-10-03" @default.
• W4229011569 title "PTD" @default.
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• W4229011569 doi "https://doi.org/10.1145/3477314.3507036" @default.
• W4229011569 hasPublicationYear "2022" @default.
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