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name: humanize-image description: Detect and remove AI fingerprints from AI-generated images. Strip metadata, add film grain, recompress, and bypass AI image detectors. Works with Midjourney, DALL-E, Stable Diffusion, Flux output. allowed-tools:

• Read
• Write
• Edit
• exec

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AI Image De-Fingerprinting Skill

Comprehensive CLI for removing AI detection patterns from AI-generated images. Transforms detectable AI images into human-camera-like photographs using multiple processing techniques.

Supported Models: Midjourney, DALL-E 3, Stable Diffusion, Flux, Firefly, Leonardo, and more.

Quick Start

# Basic processing (medium strength)
python scripts/deai.py input.png

# Specify output file
python scripts/deai.py input.png -o output.jpg

# Adjust processing strength
python scripts/deai.py input.png --strength heavy

# Only strip metadata (fastest)
python scripts/deai.py input.png --no-metadata

# Batch process directory
python scripts/deai.py input_dir/ --batch

# Pure Bash version (no Python needed)
bash scripts/deai.sh input.png output.jpg

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How It Works

AI-generated images contain multiple detection layers:

Detection Vectors

1. Metadata: EXIF tags revealing generation tool, C2PA watermarks
2. Frequency Domain: DCT coefficient patterns unique to diffusion models
3. Pixel Patterns: Over-smoothness, unnatural noise distribution
4. Visual Features: Perfect lighting, repetitive textures

Processing Pipeline

Our de-fingerprinting pipeline applies 7 transformation stages:

Input → Metadata Strip → Grain Addition → Color Adjustment → 
Blur/Sharpen → Resize Cycle → JPEG Recompress → Final Metadata Clean → Output

Stage Details

| Stage | Purpose | Technique | |-------|---------|-----------| | Metadata Strip | Remove EXIF/C2PA/JUMBF tags | ExifTool | | Grain Addition | Add camera sensor noise | Poisson/Gaussian noise overlay | | Color Adjustment | Break color distribution patterns | Contrast/saturation/brightness tweak | | Blur/Sharpen | Disrupt edge detection patterns | Gaussian blur + unsharp mask | | Resize Cycle | Introduce resampling artifacts | Downscale → upscale with Lanczos | | JPEG Recompress | Add compression artifacts | Quality 75 → 95 cycle | | Final Clean | Ensure no metadata leakage | ExifTool re-run |

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Processing Strength

Choose strength based on detection risk vs quality tradeoff:

| Strength | Description | Success Rate | Quality Loss | |----------|-------------|--------------|--------------| | light | Minimal processing, preserve quality | 35-45% | Very low | | medium | Balanced (default) | 50-65% | Low | | heavy | Aggressive processing | 65-80% | Medium |

Success rate = percentage of images passing common AI detectors (Hive, Illuminarty, AI or Not)

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Usage Examples

Single Image Processing

# Default medium strength
python scripts/deai.py ai_portrait.png

# Light processing for high-quality images
python scripts/deai.py artwork.png --strength light -o clean_artwork.jpg

# Heavy processing for stubborn detection
python scripts/deai.py midjourney_out.png --strength heavy

Batch Processing

# Process entire directory
python scripts/deai.py ./ai_images/ --batch -o ./cleaned/

# Batch with specific strength
python scripts/deai.py ./gallery/*.png --batch --strength heavy

Metadata-Only Mode

# Only strip metadata (instant, no quality loss)
python scripts/deai.py image.jpg --no-metadata

Using Bash Version

# No Python/Pillow needed, pure ImageMagick + ExifTool
bash scripts/deai.sh input.png output.jpg

# Specify strength
bash scripts/deai.sh input.png output.jpg heavy

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Dependencies

Required

• ImageMagick (7.0+) — Image processing engine
• ExifTool — Metadata manipulation
• Python 3.7+ (for deai.py)
• Pillow (Python imaging library)
• NumPy (for deai.py)

Check Installation

bash scripts/check_deps.sh

This will verify all dependencies and provide installation commands if missing.

Manual Installation

Debian/Ubuntu:

sudo apt update
sudo apt install -y imagemagick libimage-exiftool-perl python3 python3-pip
pip3 install Pillow numpy

macOS:

brew install imagemagick exiftool python3
pip3 install Pillow numpy

Fedora/RHEL:

sudo dnf install -y ImageMagick perl-Image-ExifTool python3-pip
pip3 install Pillow numpy

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Command Reference

deai.py (Python Version)

python scripts/deai.py <input> [options]

Arguments:
  input                 Input image file or directory (batch mode)

Options:
  -o, --output FILE     Output file path (default: input_deai.jpg)
  --strength LEVEL      Processing strength: light|medium|heavy (default: medium)
  --no-metadata         Only strip metadata, skip image processing
  --batch               Process entire directory
  -q, --quiet           Suppress progress output
  -v, --verbose         Show detailed processing steps

Examples:
  python scripts/deai.py image.png
  python scripts/deai.py image.png -o clean.jpg --strength heavy
  python scripts/deai.py folder/ --batch

deai.sh (Bash Version)

bash scripts/deai.sh <input> <output> [strength]

Arguments:
  input                 Input image file
  output                Output file path
  strength              light|medium|heavy (default: medium)

Examples:
  bash scripts/deai.sh input.png output.jpg
  bash scripts/deai.sh input.png output.jpg heavy

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Understanding Detection

Common AI Detectors

| Detector | Method | Bypass Rate | |----------|--------|-------------| | Hive Moderation | Deep learning model | 50-70% (medium) | | Illuminarty | Computer vision analysis | 60-75% (medium) | | AI or Not | Binary classification | 55-70% (medium) | | SynthID | Pixel-level watermark | 35-50% (heavy) | | C2PA Verify | Metadata check | 100% (metadata strip) |

What This Skill Cannot Do

❌ Not a Silver Bullet:

• Cannot guarantee 100% bypass of all detectors
• Advanced detectors (SynthID) require more aggressive processing
• New detection methods may emerge

❌ Limitations:

• Processing reduces image quality (tradeoff necessary)
• Some detectors use multiple layers (metadata + pixel + frequency)
• Extremely aggressive processing may introduce visible artifacts

✅ What It DOES Do:

• Significantly reduces detection probability (40-80%)
• Removes metadata watermarks (100% effective)
• Maintains reasonable visual quality
• Batch processes entire collections

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Verification Workflow

1. Process Image:

   python scripts/deai.py ai_image.png -o clean.jpg --strength medium
   

2. Test on Multiple Detectors:

   • Hive Moderation
   • Illuminarty
   • AI or Not

3. If Still Detected:

   • Increase strength: --strength heavy
   • Try multiple passes
   • Manual touch-ups (add slight noise in photo editor)

4. Quality Check:

   • Compare original vs processed
   • Ensure no visible artifacts
   • Verify colors/details preserved

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Advanced Usage

Custom Processing Pipeline

Edit scripts/deai.py to adjust parameters:

# Noise strength (line ~80)
noise = np.random.normal(0, 3, img_array.shape)  # Increase 3 → 5 for more grain

# Contrast adjustment (line ~95)
enhancer.enhance(1.05)  # Increase 1.05 → 1.08 for stronger effect

# JPEG quality (line ~120)
img.save(temp_path, "JPEG", quality=80)  # Decrease 80 → 70 for more compression

Combining with External Tools

# Step 1: De-fingerprint
python scripts/deai.py ai_gen.png -o step1.jpg

# Step 2: Add subtle texture overlay (GIMP/Photoshop)
# (Manual step)

# Step 3: Re-strip metadata
exiftool -all= step1_edited.jpg

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Best Practices

For Social Media

• Use medium strength (good balance)
• Output as JPEG (universal compatibility)
• Test on platform's upload flow before posting

For Professional Use

• Start with light (preserve quality)
• Manual review each output
• Keep originals in secure storage
• Document processing steps

For Research/Testing

• Use heavy for stress testing
• Compare multiple detectors
• Document success/failure patterns

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Legal & Ethical Notice

⚠️ Use Responsibly:

This tool is intended for:

• ✅ Personal creative projects
• ✅ Academic research on AI detection
• ✅ Security testing (authorized)
• ✅ Understanding detection mechanisms

DO NOT use for:

• ❌ Fraud or deception
• ❌ Impersonating human creators
• ❌ Bypassing platform policies without authorization
• ❌ Creating misleading content

Legal Risks:

• Some jurisdictions (e.g., COPIED Act 2024) may restrict watermark removal
• Platform terms of service often prohibit AI content masking
• Commercial use may have additional legal requirements

You are responsible for compliance with applicable laws and terms of service.

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Troubleshooting

"Command not found: exiftool"

# Install ExifTool
sudo apt install libimage-exiftool-perl  # Debian/Ubuntu
brew install exiftool                     # macOS

"ImportError: No module named PIL"

pip3 install Pillow numpy

"ImageMagick policy.xml blocks operation"

# Edit /etc/ImageMagick-7/policy.xml
# Change: <policy domain="coder" rights="none" pattern="PNG" />
# To:     <policy domain="coder" rights="read|write" pattern="PNG" />

Processing is slow on large images

# Pre-resize before processing
magick large.png -resize 2048x2048\> resized.png
python scripts/deai.py resized.png

Output looks too grainy/noisy

# Use light strength
python scripts/deai.py input.png --strength light

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Development

Running Tests

# Test dependency check
bash scripts/check_deps.sh

# Test single image (verbose)
python scripts/deai.py test_images/sample.png -v

# Test batch mode
mkdir test_output
python scripts/deai.py test_images/ --batch -o test_output/

Contributing

Improvements welcome! Focus areas:

• New detection bypass techniques
• Quality preservation algorithms
• Support for more image formats (HEIC, AVIF)
• Integration with detection APIs

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References

Detection Research:

• Hu, Y., et al. (2024). "Stable signature is unstable: Removing image watermark from diffusion models." arXiv:2405.07145
• IEEE Spectrum: UnMarker tool analysis

Open Source Projects:

• Synthid-Bypass — ComfyUI watermark removal
• C2PAC — C2PA metadata tools

Detection Tools:

• Hive Moderation
• Content Credentials Verify
• Google SynthID

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Version: 1.0.0
License: MIT (for educational/research use)
Maintainer: voidborne-d
Last Updated: 2026-02-23