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Saboor
batata12345678/Saboor
Saboor is a machine learning model from batata12345678. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Detect AI-generated and manipulated real estate photos using a hybrid forensic analysis approach.
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Updated Jan 28, 2026
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At a glance
- License
- mit
- Access
- Public
- Created
- Jan 28, 2026
- Updated
- Jan 28, 2026
- SHA
- a3883ad2
Model card
From the Hugging Face model README
🏠 Real Estate Image Integrity Detection System
Detect AI-generated and manipulated real estate photos using a hybrid forensic analysis approach.
Overview
This system combines three detection modules:
- MantraNet - Pixel-level forensic analysis detecting manipulation traces, noise inconsistencies, and compression artifacts
- BLIP-2 - Vision-language model for semantic reasoning and generating human-readable explanations
- Multi-Feature Forensic Analysis - Analyzes 10+ image properties including edge sharpness, texture smoothness, color saturation, lighting gradients, and noise patterns
How It Works
Detection Features
| Feature | What It Detects |
|---|---|
| Edge Density | AI renders have unnaturally sharp, uniform edges |
| Texture Smoothness | AI lacks micro-imperfections found in real materials |
| Color Saturation | AI images tend to be oversaturated with uniform colors |
| Lighting Gradients | Real photos have natural light falloff; AI is too uniform |
| Noise Patterns | Real cameras produce characteristic sensor noise |
| JPEG Artifacts | Compressed photos from phones show blocking artifacts |
| LBP Texture | Local Binary Patterns capture micro-texture differences |
Output
- Authenticity Score: 0.0 (definitely real) to 1.0 (definitely fake)
- Classification:
real,full_synthesis,splicing, orinpainting - Forensic Explanation: Detailed analysis of detected anomalies or authenticity markers
Usage
Web Interface (Hugging Face Space)
Simply upload a real estate interior photo and get instant analysis.
Command Line
python predict.py --input_dir ./images --output_file predictions.json
Python API
from predict import analyze_image, load_models
pixel_model, processor, vlm_model, device = load_models()
result = analyze_image("photo.jpg", pixel_model, processor, vlm_model, device)
print(result)
Interpretation Guide
| Score Range | Interpretation |
|---|---|
| 0.0 - 0.25 | High confidence authentic photograph |
| 0.25 - 0.45 | Likely authentic, minor uncertainty |
| 0.45 - 0.60 | Borderline / uncertain |
| 0.60 - 0.80 | Likely AI-generated or manipulated |
| 0.80 - 1.0 | High confidence AI-generated |
Technical Details
- CPU-only inference - No GPU required
- Zero-shot approach - Uses pre-trained models, no task-specific training
- Explainable - Provides specific visual evidence for decisions
Files
app.py- Gradio web interfacepredict.py- Main prediction pipelinemantranet_model.py- MantraNet model architecturerequirements.txt- Python dependencies
Citation
If you use this system, please cite:
- MantraNet: Wu et al., "ManTraNet: Manipulation Tracing Network" (CVPR 2019)
- BLIP-2: Li et al., "BLIP-2: Bootstrapping Language-Image Pre-training" (ICML 2023)
License
MIT License
- License
- mit
- Created
- Jan 28, 2026
- Updated
- Jan 28, 2026