Version 0.1 (2025) By Benoît (BSM3D) Saint-Moulin
Image Analyzer emerges as a revolutionary web application designed to tackle one of the most pressing challenges in the digital age: identifying images generated by artificial intelligence. In a world where visual content is increasingly complex and potentially deceptive, this tool offers a sophisticated, transparent approach to image authentication.
The application represents a paradigm shift in how we understand and verify digital imagery. By employing multiple analytical vectors, Image Analyzer provides users with a comprehensive toolkit for examining the origins and authenticity of visual content. It goes beyond simple detection, offering a nuanced, probabilistic assessment that empowers users to make informed judgments about the images they encounter.
The tool stands in stark contrast to conventional image detection solutions. Where many commercial products rely on invasive data collection and complex, opaque algorithms, Image Analyzer is built on a foundation of transparency, user privacy, and ethical technology development.
Key privacy principles include 100% client-side processing, meaning all analysis occurs directly within the user's browser. There is no data storage, no cloud uploads, and no registration requirements. The entire application is free to use and comes with publicly available source code, allowing for complete transparency and community verification.
- 100% Client-side processing
- No AI model training
- No data storage or cloud upload
- No registration required
- Free to use, code publicly available
Note: This tool provides probability-based analysis and should be used as part of a broader verification process, not as a standalone proof of image authenticity.
you can use and try it : https://bsm3d.com/img_analyzer/
Current Capabilities:
- Pattern detection
- Texture analysis
- Color distribution analysis
- Symmetry detection
- Noise pattern analysis
- Compression artifacts detection
Roadmap Enhancements:
- Improve detection of specialized surfaces
- Skin textures
- Metal surfaces
- Clothing materials
- Advanced image type classification
- 3D renders
- Illustrations
- Photographic images
Current Capabilities:
- Unique colors count
- Color banding detection
- Color transition analysis
- Saturation variance
Roadmap Enhancements:
- Develop advanced color pattern recognition
- Implement more sophisticated color transition analysis
Current Capabilities:
- Edge detection
- Repetitive pattern analysis
- Texture uniformity
- Natural vs. artificial noise detection
Roadmap Enhancements:
- Integrate Neural Network Detection Layers
- Develop advanced visual analysis techniques
- InvertHue stamping detection
- Blacklight analysis
- Brightness/Contrast visual checks
Current Capabilities:
- EXIF data extraction and verification
- Camera settings analysis
- Geolocation data extraction
Roadmap Enhancements:
- Crypto/Signature encoding and decoding
- Implement invisible signature for image ownership validation
Current Capabilities:
- Comparative analysis between real and AI-generated images
- Statistical pattern recognition
- Detailed comparison reports
- Custom threshold calibration
Roadmap Enhancements:
- Add External JSON configuration file
- Develop more advanced deep learning training capabilities
- Create configuration editor interface
- Minimize AI Detection API dependencies
- Ensure eco-friendly computational approach
- Optimize resource usage and efficiency
- De#teractive configuration tools
- Create engaging HTML5 banner visualization
- Enhance user interface customization
The tool provides probability-based analysis and should not be considered absolute proof.
Strengths:
- Multi-vector analysis approach
- Statistical pattern recognition
- EXIF data verification
- Color distribution analysis
Limitations:
- No 100% accuracy guarantee
- Potential false positives/negatives
- Rapidly evolving AI technology
- Dependency on image quality
-
EXIF Data
- Presence of EXIF data suggests a real photo
- Complete camera settings are strong indicators
- Advanced users can potentially spoof EXIF data
-
Color Analysis
- Real photos typically have more unique colors
- AI generations tend to optimize and reduce color count
- Analyze natural color transitions
-
Noise Patterns
- Natural photos have random noise patterns
- AI-generated images often show regular noise patterns
- Suspicious perfect transitions
- Upload image for quick analysis
- Review overall probability score
- Check detected indicators
- Examine EXIF data
- Load multiple reference images
- Generate comparative analysis
- Review detailed statistical reports
- Use for higher precision requirements
- Combine tool results with human judgment
- Consider context and image source
- Use multiple detection methods
- Maintain critical analysis
- Modern web browser
- JavaScript enabled
- Minimum 4GB RAM recommended
Free to use, source code available on GitHub
© 2025 Benoît Saint-Moulin (BSM3D) All rights reserved.
GitHub: @bsm3d