Likelihood ratio estimation for authorship text evidence: An empirical comparison of score- and feature-based methods

Ishihara, Shunichi; Carne, Michael

doi:10.1016/j.forsciint.2022.111268

Cited by 9 publications

(8 citation statements)

References 71 publications

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“…While the research into various aspects of automated systems for various tasks related to handwriting and signature analysis is significant, one relevant textbook [ 190 ] and104 articles [ [191] , [192] , [193] , [194] , [195] , [196] , [197] , [198] , [199] , [200] , [201] , [202] , [203] , [204] , [205] , [206] , [207] , [208] , [209] , [210] , [211] , [212] , [213] , [214] , [215] , [216] , [217] , [218] , [219] , [220] , [221] , [222] , [223] , [224] , [225] , [226] , [227] , [228] , [229] , [230] , [231] , [232] , [233] , [234] , [235] , [236] , [237] , [238] , [239] , [240] , [241] , [242] , [243] , [244] , [245] , [246] , [247] , [248] , [249] , [250] , [251] , [252] , ...…”

Section: Forensic Handwriting Examinationmentioning

confidence: 99%

“…Ishihara and Carne [ 194 ] continued this research by comparing the score-based (univariate) method to three features-based (multivariate) methods built on Poisson models for estimating the LRs of textual evidence. The log-likelihood ratio costs indicated that the features-based methods outperform the score-based method, and a selection of features can improve the performance for the feature-based methods.…”

Section: Forensic Handwriting Examinationmentioning

confidence: 99%

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Interpol questioned documents review 2019–2022

Deviterne-Lapeyre

Ibrahim²

2023

Forensic Science International: Synergy

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Section: Forensic Handwriting Examinationmentioning

confidence: 99%

Section: Forensic Handwriting Examinationmentioning

confidence: 99%

Interpol questioned documents review 2019–2022

Deviterne-Lapeyre

Ibrahim²

2023

Forensic Science International: Synergy

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“…However, analyses based on an expert linguist's opinion have been criticized for lacking validation (Juola 2021). Even where textual evidence is measured quantitatively and analyzed statistically, the interpretation of the analysis has rarely been based on the LR framework (c.f., Ishihara 2017Ishihara , 2021Ishihara , 2023Ishihara and Carne 2022;Nini 2023).…”

Section: Introduction 1background and Aimsmentioning

confidence: 99%

Validation in Forensic Text Comparison: Issues and Opportunities

Ishihara,

Kulkarni,

Carne

et al. 2024

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It has been argued in forensic science that the empirical validation of a forensic inference system or methodology should be performed by replicating the conditions of the case under investigation and using data relevant to the case. This study demonstrates that the above requirement for validation is also critical in forensic text comparison (FTC); otherwise, the trier-of-fact may be misled for their final decision. Two sets of simulated experiments are performed: one fulfilling the above validation requirement and the other overlooking it, using mismatch in topics as a case study. Likelihood ratios (LRs) are calculated via a Dirichlet-multinomial model, followed by logistic-regression calibration. The derived LRs are assessed by means of the log-likelihood-ratio cost, and they are visualized using Tippett plots. Following the experimental results, this paper also attempts to describe some of the essential research required in FTC by highlighting some central issues and challenges unique to textual evidence. Any deliberations on these issues and challenges will contribute to making a scientifically defensible and demonstrably reliable FTC available.

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“…The LR framework provides a systematic framework for the quantitative comparison of the evidence's support for each of two forensic propositions or hypotheses [11,12]. LRs have been widely applied in the context of DNA evidence [13], and there is ongoing research into constructing LR models for a wide variety of other evidence types [14][15][16][17][18][19][20]. For quantitative evaluations using user-generated digital event data, recent work has also adopted the LR framework [1][2][3]8].…”

Section: Introductionmentioning

confidence: 99%

Likelihood ratios for changepoints in categorical event data with applications in digital forensics

Longjohn,

Smyth

2024

Journal of Forensic Sciences

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We investigate likelihood ratio models motivated by digital forensics problems involving time‐stamped user‐generated event data from a device or account. Of specific interest are scenarios where the data may have been generated by a single individual (the device/account owner) or by two different individuals (the device/account owner and someone else), such as instances in which an account was hacked or a device was stolen before being associated with a crime. Existing likelihood ratio methods in this context require that a precise time is specified at which the device or account is purported to have changed hands (the changepoint)—this is the known changepoint likelihood ratio model. In this paper, we develop a likelihood ratio model that instead accommodates uncertainty in the changepoint using Bayesian techniques, that is, an unknown changepoint likelihood ratio model. We show that the likelihood ratio in this case can be calculated in closed form as an expression that is straightforward to compute. In experiments with simulated changepoints using real‐world data sets, the results demonstrate that the unknown changepoint model attains comparable performance to the known changepoint model that uses a perfectly specified changepoint, and considerably outperforms the known changepoint model that uses a misspecified changepoint, illustrating the benefit of capturing uncertainty in the changepoint.

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Likelihood ratio estimation for authorship text evidence: An empirical comparison of score- and feature-based methods

Cited by 9 publications

References 71 publications

Interpol questioned documents review 2019–2022

Interpol questioned documents review 2019–2022

Validation in Forensic Text Comparison: Issues and Opportunities

Likelihood ratios for changepoints in categorical event data with applications in digital forensics

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