Humanities & Social Sciences Communications (Aug 2022)

A new uniform framework of source attribution in forensic science

  • Zhihui Li,
  • Yao Liu,
  • Xiyuan Hu,
  • Guiqiang Wang

Journal volume & issue
Vol. 9, no. 1
pp. 1 – 11


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Abstract Scientific principles of forensic source identification have attracted widespread interest in recent years. Among those presented principles and theorems, the Bayes inference was regarded as one of the most scientific principles. In this paper, we argue that the Bayes theorem is in challenge when used as principal basis for forensic source identification. Furthermore, two novel concepts: feature-matching value and feature-matching identification value are proposed inspired by the basic ideas of information theory. Based on these two concepts, a new framework is established to describe the source identification principles of forensic science. The proposed source identification principle uses deduction logic structure, and unifies the three existing source identification paradigms. The newly proposed framework is expected to provide a solid scientific basis for the source attribution methods in forensic science.