# dfd-tud/deda

Repository: https://github.com/dfd-tud/deda
Canonical: https://ross.abutalabs.com/products/deda
Homepage: https://dfd.inf.tu-dresden.de
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: yellow-dots, tracking-dots, printer-forensic
Last push: 2024-09-15T13:38:37+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3129, "days_push": 717, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2565, forks 119 (observed 2026-08-28T04:07:01.130547+00:00)

## What it is
DEDA is a Python toolkit for extracting, decoding, and anonymising the yellow tracking dots that colour laser printers embed in printouts. It provides both CLI tools and a GUI for forensic analysis and privacy protection of printed documents.

## Use cases
- decode printer tracking dots from a scanned document
- identify which laser printer printed a document
- anonymise a PDF before printing to remove tracking dots
- compare scans to find which printer differs from a set
- extract unknown yellow dot patterns for analysis
- create custom tracking dot matrices in a PDF

## When to choose
- you need to read or decode Machine Identification Code (MIC) yellow dots from scans
- you want to anonymise printouts against printer tracking
- you are doing printer forensics research on scanned documents

## When to avoid
- you need inkjet or printer forensics beyond colour laser tracking dots
- you want a polished end-user product rather than research tooling
- you need Windows support for the anonymisation mask workflow (wand is Unix-only)

## Facets
- artifact type: cli-tool
- maturity: maintenance
- function: image-processing, security, privacy, ocr, developer-tools
- domain: security, privacy, pdf, image-processing
- platform: python, cli
- tags: yellow-dots, printer-forensics, tracking-dots, anonymisation, document-forensics, gui, forensics, linux, macos

## Member repositories
- dfd-tud/deda (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:01.130547+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:23:24.833205+00:00, confidence not recorded.
  - readme: https://github.com/dfd-tud/deda (fetched 2026-08-28T04:07:01.130547+00:00, sha 126af9055903)
  - homepage: https://dfd.inf.tu-dresden.de (fetched 2026-08-29T10:05:53.970484+00:00, sha fd20c6d07213)
  - registry_pypi: https://pypi.org/pypi/deda/json (fetched 2026-08-29T10:05:53.979626+00:00, sha bc7cb292bae6)
- Data as of 2026-08-30T08:39:29.467469+00:00.