Source Evidence, Derived Data and AI Output in Digital Forensics

A technical provenance model that separates original evidence, deterministic transformations, model inference, and analyst interpretation in digital-forensic workflows.

Transforming an artefact for analysis and asking a model to infer something from it are different operations. Treating both as generic “processed data” makes it difficult to tell later which information came from the source and which was introduced by an algorithm.

A useful forensic pipeline separates four layers:

original source
    ↓
deterministic / traceable transformation
    ↓
model inference
    ↓
analyst interpretation

Original source

The acquired file, disk image, packet capture, or other artefact should remain preserved as the source object. Hashes, acquisition metadata, origin, and chain-of-custody information belong to this layer. Producing a convenient representation does not replace the source.

For example, decoding a G.711 WAVE recording into a PCM16 working copy can make listening or ASR practical. The PCM is derived data; it is not the original WAVE evidence.

Deterministic derived data

A transformation that produces the same output from the same input and parameters can be reproduced and audited. Software version, parameters, output hash, and transformation order can be recorded.

Resampling, channel selection, container decoding, or lossless format conversion may belong here. Deterministic does not mean identical in evidential status; the output is still derived.

Model inference

OCR text, an ASR transcript, face or speaker embeddings, classification scores, and detections are not fields stored directly in the source. A model produces an estimate or representation from observed data. The result can depend on model version, preprocessing, thresholds, and runtime details.

A useful record therefore includes:

model and version,
preprocessing chain,
inference parameters,
threshold or decision rule,
score or distance,
where practical, the hash of the working copy used for inference.

Generative enhancement is a separate risk class

Denoising and restoration can alter spectral or visual information. Generative restoration can synthesize detail not directly present in the source observation. Such output may be useful for exploration, but newly generated detail must not silently become a statement about what the source itself contained.

The defensible description is not merely “the image was enhanced.” The process, what was visible in the original, and what became visible only after transformation should remain distinguishable.

Analyst interpretation

The final layer is human interpretation. A similarity score is not by itself a conclusion that two identities are the same; an ASR transcript is not the recording itself. Model output is assessed with other findings, with the source available for review.

This boundary does not reduce the value of AI. It makes AI-assisted analysis more reproducible because every result has a defined evidential role.