Overview
- Skill Key
- cassh100k/image-nuke
- Author
- cassh100k
- Source Repo
- openclaw/skills
- Version
- -
- Source Path
- skills/cassh100k/image-nuke
- Latest Commit SHA
- 792e7b73c80e20376e08863d7c2f2455e7d6f2b9
Nuclear-grade image metadata cleanser. Strip ALL EXIF/GPS/camera data, re-encode with noise injection. Forensically untraceable, reverse image search resistant.
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直接复制以下提示词,发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 image-nuke 技能。 若已安装,则直接安装 image-nuke 技能。
# 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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