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Multi-Embed Nonlinear Scale-Space for Image Trust Root Generation
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DOI：10.1109/CC.2016.7781727.png)
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Abstract
En 中文
An image trust root is a special type of soft trust root for trusted computing. However, image trust root generation is difficult, as it needs a corresponding stable logic feature generation model and algorithm for dynamical and sustained authentication. This paper proposes a basic function of constructing new scale-spaces with deep detecting ability and high stability for image features aimed at image root generation. According to the heat distribution and spreading principle of various kinds of infinitesimal heat sources in the space medium, a multi-embed nonlinear diffusion equation that corresponds to the multi-embed nonlinear scale-space is proposed, a HARRIS-HESSIAN scale-space evaluation operator that aims at the structure acceleration characteristics of a local region and can make use of image pixels' relative spreading movement principle was constructed, then a single-parameter global symmetric proportion (SPGSP) operator was also constructed. An authentication test with 3000 to 5000 cloud entities shows the new scale-space can work well and is stable, when the whole cloud has 5%-50% behavior with un-trusted entities. Consequently, it can be used as the corresponding stable logic feature generation model and algorithm for all kinds of images, and logic relationships among image features for trust roots.
Keywords:
image trust root
scale-space
diffusion equation
evolution operator
feature detection
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