Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Deai Image
Detect and remove AI fingerprints from AI-generated images. Strip metadata, add film grain, recompress, and bypass AI image detectors. Works with Midjourney,...
skill openclawclawhub Free
Detect and remove AI fingerprints from AI-generated images. Strip metadata, add film grain, recompress, and bypass AI image detectors. Works with Midjourney,...
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0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, package.json, scripts/check_deps.sh, scripts/deai.py, scripts/deai.sh
Validation
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- Review SKILL.md after the package is downloaded.
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New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- swaylq
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Documentation
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
How It Works
AI-generated images contain multiple detection layers:
Detection Vectors
Metadata: EXIF tags revealing generation tool, C2PA watermarks Frequency Domain: DCT coefficient patterns unique to diffusion models Pixel Patterns: Over-smoothness, unnatural noise distribution Visual Features: Perfect lighting, repetitive textures
Processing Pipeline
Our de-fingerprinting pipeline applies 7 transformation stages: Input β Metadata Strip β Grain Addition β Color Adjustment β Blur/Sharpen β Resize Cycle β JPEG Recompress β Final Metadata Clean β Output Stage Details StagePurposeTechniqueMetadata StripRemove EXIF/C2PA/JUMBF tagsExifToolGrain AdditionAdd camera sensor noisePoisson/Gaussian noise overlayColor AdjustmentBreak color distribution patternsContrast/saturation/brightness tweakBlur/SharpenDisrupt edge detection patternsGaussian blur + unsharp maskResize CycleIntroduce resampling artifactsDownscale β upscale with LanczosJPEG RecompressAdd compression artifactsQuality 75 β 95 cycleFinal CleanEnsure no metadata leakageExifTool re-run
Processing Strength
Choose strength based on detection risk vs quality tradeoff: StrengthDescriptionSuccess RateQuality LosslightMinimal processing, preserve quality35-45%Very lowmediumBalanced (default)50-65%LowheavyAggressive processing65-80%Medium Success rate = percentage of images passing common AI detectors (Hive, Illuminarty, AI or Not)
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
Required
ImageMagick (7.0+) β Image processing engine ExifTool β 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
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
Common AI Detectors
DetectorMethodBypass RateHive ModerationDeep learning model50-70% (medium)IlluminartyComputer vision analysis60-75% (medium)AI or NotBinary classification55-70% (medium)SynthIDPixel-level watermark35-50% (heavy)C2PA VerifyMetadata check100% (metadata strip)
What This Skill Cannot Do
β Not a Silver Bullet: Cannot guarantee 100% bypass of all detectors Advanced detectors (SynthID) require more aggressive processing New detection methods may emerge β Limitations: Processing reduces image quality (tradeoff necessary) Some detectors use multiple layers (metadata + pixel + frequency) Extremely aggressive processing may introduce visible artifacts β What It DOES Do: Significantly reduces detection probability (40-80%) Removes metadata watermarks (100% effective) Maintains reasonable visual quality Batch processes entire collections
Verification Workflow
Process Image: python scripts/deai.py ai_image.png -o clean.jpg --strength medium Test on Multiple Detectors: Hive Moderation Illuminarty AI or Not If Still Detected: Increase strength: --strength heavy Try multiple passes Manual touch-ups (add slight noise in photo editor) Quality Check: Compare original vs processed Ensure no visible artifacts Verify colors/details preserved
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 β 5 for more grain # Contrast adjustment (line ~95) enhancer.enhance(1.05) # Increase 1.05 β 1.08 for stronger effect # JPEG quality (line ~120) img.save(temp_path, "JPEG", quality=80) # Decrease 80 β 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
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
Legal & Ethical Notice
β οΈ Use Responsibly: This tool is intended for: β Personal creative projects β Academic research on AI detection β Security testing (authorized) β Understanding detection mechanisms DO NOT use for: β Fraud or deception β Impersonating human creators β Bypassing platform policies without authorization β 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.
"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
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
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 β ComfyUI watermark removal C2PAC β C2PA metadata tools Detection Tools: Hive Moderation Content Credentials Verify Google SynthID Version: 1.0.0 License: MIT (for educational/research use) Maintainer: voidborne-d Last Updated: 2026-02-23
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Scripts2 Docs1 Config
- SKILL.md
- README.md
- scripts/check_deps.sh
- scripts/deai.py
- scripts/deai.sh
- package.json