magika_output.md

Magika Output Documentation

The command line tool and the various APIs (in Python, JS, Rust, etc.) all return the same information.

Consider the following example:

$ magika tests_data/basic/javascript/code.js --json
[
  {
    "path": "tests_data/basic/javascript/code.js",
    "result": {
      "status": "ok",
      "value": {
        "dl": {
          "description": "JavaScript source",
          "extensions": [
            "js",
            "mjs",
            "cjs"
          ],
          "group": "code",
          "is_text": true,
          "label": "javascript",
          "mime_type": "application/javascript"
        },
        "output": {
          "description": "JavaScript source",
          "extensions": [
            "js",
            "mjs",
            "cjs"
          ],
          "group": "code",
          "is_text": true,
          "label": "javascript",
          "mime_type": "application/javascript"
        },
        "score": 0.9710000157356262
      }
    }
  }
]

This is how to interpret the output:

• path is simply the file path this prediction is referring to (relevant when scanning multiple files at the same time).
• result.status indicates whether magika was able to scan the sample. ok means all was good, in which case a value field is present with the details about the output.
• score indicates the confidence of the prediction.
• the dl block returns information about the prediction with the deep learning model. In this case, the model predicted javascript.
• the output block returns information about the prediction of "Magika the tool", which considers a number of aspects such as the prediction of the deep learning model, its confidence, and the indicated "prediction mode" (see --prediction-mode). In this example, the model's confidence was high enough to be trustworthy, and thus the output of the "Magika the tool" matches the content type inferred by the deep learning model.
• the dl and output blocks contain a number of metadata about the predicted content type, such as a label suitable for automated processing, a human-readable description, MIME Type, a list of extensions usually associated with the predicted content type, a high-level group, and a is_text boolean that indicates whether the type is textual or not.

In case the model's confidence is low, the output from "Magika the tool" would be a generic content type, such as "Generic text document" or "Unknown binary data".

Note that the deep learning model is not used in all cases, e.g., if a file is too small (currently less than 16 bytes), in which case the label of the dl block is set to ContentTypeLabel.UNDEFINED.

We expect most clients to just use output.label; we return the raw prediction of the deep learning model mostly for debugging.

See also the FAQs about why it may be appropriate to integrate Magika's results by focusing on label, rather than description, mime_type, or other output formats.