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// Skill profile

Image Nuke - Nuclear Metadata Cleanser

name: image-nuke

by cassh100k · published 2026-03-22

图像生成数据处理
Total installs
0
Stars
★ 0
Last updated
2026-03
// Install command
$ claw add gh:cassh100k/cassh100k-image-nuke
View on GitHub
// Full documentation

---

name: image-nuke

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

metadata:

openclaw:

emoji: "☢️"

requires:

bins: ["python3"]

---

# 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)
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