The study evaluates the impact of synthetic images on Morphing Attack Detection (MAD) using a Siamese network. Results show that training MAD with EfficientNetB0 from FERET, FRGCv2, and FRLL databases reduces error rates compared to state-of-the-art. However, training solely with synthetic images leads to worse performance. A mixed approach combining synthetic and digital images may enhance MAD accuracy. The research highlights the need to include synthetic images in training processes for improved detection capabilities.
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arxiv.org
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