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Entropy-Based Counter-Deception in Information Fusion

机译:信息融合中基于熵的反欺骗

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In this paper, we develop an entropy-based degree of falsity and combine it with a previously developed conflict-based degree of falsity in order to grade all belief functions. The new entropy-based degree of falsity is based on observing changes in entropy that are not consistent with combining only truthful information. With this measure, we can identify deliberately deceptive information and exclude it from the information fusion process.
机译:在本文中,我们开发了基于熵的虚假度,并将其与先前开发的基于冲突的虚假度相结合,以便对所有置信函数进行评分。新的基于熵的虚假度基于观察到的熵变化,该变化与仅组合真实信息不一致。通过这种措施,我们可以识别故意欺骗性的信息,并将其从信息融合过程中排除。

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