# chishaxie/BlindWaterMark

盲水印 by python

Repository: https://github.com/chishaxie/BlindWaterMark
Canonical: https://ross.abutalabs.com/products/chishaxie-blindwatermark
Language: Python
License: GPL-3.0
License Family: copyleft
Last push: 2025-05-06T02:53:17+00:00

## Health v2 (maintenance only)
Score: 41/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 20, release rhythm 35, longevity 100
- inputs: {"age_days": 3624, "days_push": 484, "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 1106, forks 206 (observed 2026-08-28T04:03:36.474574+00:00)

## What it is
A Python command-line tool that embeds invisible blind watermarks into images and extracts them later using frequency-domain techniques. Decoding requires the original unwatermarked image to recover the watermark.

## Use cases
- embed an invisible watermark in an image to prove ownership
- extract a hidden watermark from an image given the original
- add copyright protection to photos before publishing
- verify whether an image was leaked from a specific source
- hide a logo or text image inside a picture imperceptibly

## When to choose
- you need blind watermarking where the watermark is invisible to viewers
- you have access to the original image when decoding
- you want a simple Python CLI without heavy dependencies

## When to avoid
- you need robust watermarking that survives compression, cropping, or resizing
- you don't have the original image available for extraction
- you need a library API rather than a command-line script

## Facets
- artifact type: cli-tool
- maturity: maintenance
- function: image-processing, cryptography, cli
- domain: image-processing, security, privacy
- platform: python, cli, cross-platform
- tags: blind-watermark, watermarking, steganography, digital-rights, frequency-domain

## Member repositories
- chishaxie/BlindWaterMark (main) score 41

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:36.474574+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 2026-08-30T06:44:13.912322+00:00, confidence not recorded.
  - readme: https://github.com/chishaxie/BlindWaterMark (fetched 2026-08-28T04:03:36.474574+00:00, sha ef5d86df3410)
- Data as of 2026-08-30T08:39:29.467469+00:00.