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Virtual sample generation for few-shot source camera identification
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PRE
AI
DOI：10.1016/j.jisa.2022.103153.png)
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Abstract
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The Source Camera Identification (SCI) has achieved remarkable success. However, existing approachesrequire sufficiently large training sets for high performance on accuracy and robustness. For maintaining highperformance given small training sets, we propose a semi-supervised, Mega-Trent-Diffusion (MTD) method togenerate virtual samples, such that the training sets can be expanded and unlabeled samples can be fullyutilized as well. The stability of our method is improved using ensemble learning. Our theoretical analysis andexperiments corroborate the effectiveness of our method beyond others when few-shot is given
Keywords:
Source Camera Identification (SCI)
Few-shot
Virtual sample
Ensemble learning
Semi-supervised
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OPTICS EXPRESS
OAAI
OPTICS EXPRESSIF3.3
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