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image-nuke

Nuclear-grade image metadata cleanser. Strip ALL EXIF/GPS/camera data, re-encode with noise injection. Forensically untraceable, reverse image search resistant.

cassh100k0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install cassh100k/image-nuke

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Image Nuke - Nuclear Metadata Cleanser

Strip everything. Re-encode. Inject noise. Forensically untraceable.

What Gets Destroyed

  • ALL EXIF data (camera, lens, exposure, timestamps, software)
  • GPS / location coordinates
  • ICC color profiles
  • XMP / IPTC metadata
  • Adobe tags and editing history
  • Embedded thumbnails

Nuclear Operations

  • Sub-pixel Gaussian noise injection (invisible to human eye)
  • Micro color shift (undetectable hue rotation)
  • Per-pixel brightness variation
  • Random micro-crop (changes dimensions by 1-3px)
  • Fresh JPEG re-encoding with randomized quality/subsampling
  • Different perceptual hash (reverse image search resistant)

Usage

# Single image - nuclear mode
python3 {baseDir}/scripts/nuke_image.py photo.jpg

# Custom output + max noise
python3 {baseDir}/scripts/nuke_image.py photo.jpg clean.jpg --noise 5

# Batch process entire directory
python3 {baseDir}/scripts/nuke_image.py --batch ./photos/ ./clean/

# Lower quality for harder reverse matching
python3 {baseDir}/scripts/nuke_image.py photo.jpg --quality 80 --noise 4

Noise Levels

Level Sigma Use Case
1 0.8 Light cleanse - metadata only feel
2 1.6 Standard - good balance
3 2.4 Default - recommended
4 3.2 Heavy - reverse search resistant
5 4.0 Nuclear - maximum anonymization

Requirements

  • Python 3
  • Pillow (pip install Pillow)
  • NumPy (pip install numpy)

Notes

  • Output is always JPEG (even if input is PNG)
  • Original file is never modified
  • Each run produces a unique output (randomized noise)

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/cassh100k/image-nuke
Version
—
Commit
792e7b73c80e20376e08863d7c2f2455e7d6f2b9
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

SKILL.md
# Image Nuke - Nuclear Metadata Cleanser

Strip everything. Re-encode. Inject noise. Forensically untraceable.

## What Gets Destroyed

- ALL EXIF data (camera, lens, exposure, timestamps, software)
- GPS / location coordinates
- ICC color profiles
- XMP / IPTC metadata
- Adobe tags and editing history
- Embedded thumbnails

## Nuclear Operations

- Sub-pixel Gaussian noise injection (invisible to human eye)
- Micro color shift (undetectable hue rotation)
- Per-pixel brightness variation
- Random micro-crop (changes dimensions by 1-3px)
- Fresh JPEG re-encoding with randomized quality/subsampling
- Different perceptual hash (reverse image search resistant)

## Usage

```bash
# Single image - nuclear mode
python3 {baseDir}/scripts/nuke_image.py photo.jpg

# Custom output + max noise
python3 {baseDir}/scripts/nuke_image.py photo.jpg clean.jpg --noise 5

# Batch process entire directory
python3 {baseDir}/scripts/nuke_image.py --batch ./photos/ ./clean/

# Lower quality for harder reverse matching
python3 {baseDir}/scripts/nuke_image.py photo.jpg --quality 80 --noise 4
```

## Noise Levels

| Level | Sigma | Use Case |
|-------|-------|----------|
| 1 | 0.8 | Light cleanse - metadata only feel |
| 2 | 1.6 | Standard - good balance |
| 3 | 2.4 | Default - recommended |
| 4 | 3.2 | Heavy - reverse search resistant |
| 5 | 4.0 | Nuclear - maximum anonymization |

## Requirements

- Python 3
- Pillow (`pip install Pillow`)
- NumPy (`pip install numpy`)

## Notes

- Output is always JPEG (even if input is PNG)
- Original file is never modified
- Each run produces a unique output (randomized noise)

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