Chapter 3 — Study design, ethics and reproducibility
Lesson map
Lesson 03 published. Map, example, EX015.
flowchart TD
N0["Learning outcomes"]
N1["3.1 Real or synthetic data"]
N2["3.2 Ethics approval"]
N3["3.3 Ground truth"]
N4["3.4 Measures"]
N5["3.5 Controls"]
N6["3.6 Reproducibility plan"]
N7["3.7 Tool validation"]
N0 --> N1 --> N2 --> N3 --> N4 --> N5 --> N6 --> N7
N7 --> EX["Worked example"]
EX --> Q["Exercise EX015"]
Logical drawing
flowchart LR
N0["Learning outcomes"]
N1["3.1 Real or synthetic data"]
N2["3.2 Ethics approval"]
N3["3.3 Ground truth"]
N4["3.4 Measures"]
N5["3.5 Controls"]
N6["3.6 Reproducibility plan"]
N7["3.7 Tool validation"]
N0 --> N1 --> N2 --> N3 --> N4 --> N5 --> N6 --> N7 --> Q["EX015"]
[Learning outcomes]
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v
[3.1 Real or synthetic data]
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v
[3.2 Ethics approval]
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v
[3.3 Ground truth]
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v
[3.4 Measures]
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v
[3.5 Controls]
|
v
[3.6 Reproducibility plan]
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v
[3.7 Tool validation]
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v
[Worked example] --> [Exercise EX015]
Learning outcomes
You will (1) choose between real and synthetic evidence; (2) obtain ethical and legal approval; (3) design measurements of reduction and misses; (4) plan for reproducibility.
3.1 Real or synthetic data
Real casework images hold personal data and are restricted by law and by court orders. Garfinkel et al. (2009) argued for standardised, shareable forensic corpora so that results can be compared. Tomás uses synthetic laptop images he builds: a clean OS install, standard applications and planted user documents, so the ground truth is known. Building images himself means every file's origin and label are known before filtering, so misses can be counted exactly. The cost is realism: synthetic images lack the clutter of real laptops, which he records as a limitation.
3.2 Ethics approval
Even synthetic work needs ethics review if it involves people (an examiner agreement study does). He applies to the university committee, describing consent for examiner participants and confirming no casework data leave the police unit. His application explains that examiners will classify events from synthetic images only, that their results will be anonymised, and that they can withdraw. It confirms that his police role gives him no access to the study's data beyond what any participant would see. The committee approves the application before any examiner is recruited, and he keeps the approval letter in the project archive.
3.3 Ground truth
Every planted file is labelled "relevant" or "not relevant" before filtering. A miss is a relevant file set aside as known. Labelling before filtering prevents hindsight from changing what counts as relevant. Tomás records each label with the reason, such as a user document related to the synthetic scenario or a legitimate tool planted with illegitimate use. Designed misses are labelled relevant from the start. When a check prompted by his committee found that one planted manual was identical to an NSRL entry, he relabelled it as a designed miss with a documented reason rather than deleting it. The labels are archived with each image's seed, so anyone rebuilding an image also gets the ground truth used to count its misses.
3.4 Measures
Reduction = known files ÷ all files. Miss rate = relevant files set aside ÷ relevant files. Both are reported with counts. Reporting counts as well as percentages lets readers see how many files a figure is based on: a miss rate from a handful of relevant files means less than one from hundreds. The two measures pull in opposite directions, since a filter that sets aside more files raises reduction but may also raise misses. Tomás reports both for each image so that neither is presented alone. His results tables show the file counts behind every reduction and miss percentage, image by image.
3.5 Controls
He fixes the reference set version and hashing tool version, and records them, because a newer reference set changes results. Freezing these versions turns the reference set into a controlled condition rather than a moving one. Without it, a release added during the study could change which files count as known, and differences between images could reflect the date they were processed. Tomás records the version and hash tool in every log entry. His pilot later showed how strongly reduction depends on how current the reference set is, which made the control even more important.
3.6 Reproducibility plan
Scripts, image build recipes, seed values and the reference set version are archived; every result traces to a script run. Seed values mean the image builder produces the same files each time, so another researcher can recreate the images rather than trusting his description. Tomás tests the plan during the pilot by rebuilding an image from the archive and checking that file hashes match. The archive is released with the thesis, apart from the examiners' raw responses, which stay anonymised under the ethics approval.
3.7 Tool validation
NIST's Computer Forensics Tool Testing programme publishes test methods and reports; Tomás checks his hashing tool against known test vectors before use. A hashing error would make files appear unknown or, worse, known when they are not, so tool validation protects both measures. Tomás records which test vectors he used, the tool version and the results. When the pilot produced an unexpectedly low reduction on one image, he could rule out tool failure partly because the tool had passed these checks, and a second tool gave identical hashes. The validation record is part of the archive, so examiners can check the tool version against the test vectors he used.
Data management plan
The plan lists where images and scripts are stored, who can access them, and how long they are kept. Because the images are synthetic, they can be published after the study.
Worked example
His committee asks whether planted "relevant" files could accidentally equal published files. He checks: one planted PDF was a vendor manual identical to an NSRL entry. He relabels it carefully as a designed miss, documenting why, rather than deleting it.
Synthetic case. The committee exchange is invented.
Errors and limits
Synthetic images can lack the clutter of real laptops; reduction measured on them may overstate real-world effect.
Exercises
- EX015 — Why synthetic. Give two reasons for synthetic images here. Rubric: 3 = no personal data, known ground truth; 2 = one; 1 = cheaper; 0 = none.
- EX016 — Ethics trigger. Which part of the study needs human-participant ethics review? Rubric: 3 = examiner agreement study, consent; 2 = partial; 1 = none needed; 0 = none.
- EX017 — Miss rate. Define the miss rate formula. Rubric: 3 = relevant set aside ÷ relevant; 2 = partial; 1 = wrong ratio; 0 = none.
- EX018 — Version control. Why record the reference set version? Rubric: 3 = new versions change matches and results; 2 = partial; 1 = habit; 0 = none.
- EX019 — Manual PDF. How was the vendor manual handled, and why? Rubric: 3 = kept as designed miss, documented; 2 = partial; 1 = deleted; 0 = none.
- EX020 — Clutter. State the external-validity risk of synthetic images. Rubric: 3 = less clutter may overstate reduction; 2 = vague; 1 = minimal attempt; 0 = none.
Synthesis
Known ground truth, fixed versions, ethics approval and archived scripts turn a triage idea into a defensible study.
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