Examination of customized questioned digital documents
Bibliographic record
Abstract
With the increasing trend of digitization of business processes and personal communication across the globe, digital documents of intrinsic value continue to be created. Whereas the questioned document examination (QDE) field of forensic science deals with the examination of "physical" documents potentially disputed in a court of law, there are no developed approaches for handling questioned digital documents (QDDs). Although techniques that address related problems such as identifying document types and image forensics exist, concrete strategies for analyzing questioned "digital" documents still need to be developed. This paper focuses on developing methods to examine QDDs that are customized from a database, due to the versatile use of customized documents in many areas. As a basis for our approach, we make the case for the need to develop analysis techniques for a digital counterpart of QDE which we term Questioned Digital Document Examination (QDDE). We posit that there is a benefit in considering digital aspects of forensic science disciplines where the questions answered by the discipline are clear, from a digital perspective. The paper describes some of the aspects that can be considered in the domain of question digital document examination. In designing methods for QDDE, we discuss the process of document recreation and describe the feasibility of our recreation process in different scenarios. Our experiments show that an alternative approach of considering digital aspects from a well-defined physical domain is worthwhile. It also supports the practical application of our approach in examining documents customized from a database.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".