期刊论文

Zhang, LB

英文

IET Image Processing

1751-9659

2024

18

2

470-480

10.1049/ipr2.12962

This work was supported in part by a project supported by the National Natural Science Foundation of China under Grant 62072055, 92067104, 62172059, U1936115; Natural Science Foundation of Hunan Province under Grant 2022JJ50318; Natural Science Foundation [62072055, 92067104, 62172059, U1936115]; National Natural Science Foundation of China [2022JJ50318]; Natural Science Foundation of Hunan Province [2023A1515011575]; Natural Science Foundation of Guangdong Province [22A0200]; Scientific Research Fund of Hunan Province Education Department of China

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Here, an adaptive de‐morphing factor framework (ADFF) is proposed to restore the accomplice's facial image. A pioneering de‐morphing factor prediction network is proposed, which can better handle the variations in the degree of morphing across different images, to improve the quality of restored accomplice's facial images. Abstract Morphing attacks (MAs) pose a substantial security threat to the Automatic Border Control (ABC) system. While a few morphing attack detection (MAD) methods have been proposed, the face morphing accomplice's facial ...