Redacted text detection using neural image segmentation methods
Bibliographic record
Abstract
The redaction of sensitive information in documents is common practice in specific types of organizations. This happens for example in court proceedings or in documents released under the Freedom of Information Act (FOIA). The ability to automatically detect when information has been redacted has several practical applications, such as the gathering of statistics on the amount of redaction present in documents, enabling a critical view on redaction practices. It can also be used to further investigate redactions, and whether or not the used techniques provide sufficient anonymization. The task is particularly challenging because of the large variety of redaction methods and techniques, from software for automatic redaction to manual redactions by pen. Any detection system must be robust to a large variety of inputs, as it will be run on many documents that might not even contain redactions. In this study, we evaluate two neural methods for the task, namely a Mask R-CNN model and a Mask2Former model, and compare them to a rule-based model based on optical character recognition and morphological operations. The best performing, the Mask R-CNN model, has a recall of .94 with a precision of .96 over a challenging data set containing several redaction types. Adding many pages without redaction barely lowers this score (precision drops to .90, recall drops to .92). The Mask2Former model is most robust to inputs without redactions, producing the least false positives of all models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".