🕵️♀️ GenAI Real Estate Forensic Detector (Track B)
Status: 🏆 Competition Submission for MenaML Winter School 2026
Track: B (Real Estate & Commercial Integrity)
Model Architecture: EfficientNetB0 (Fine-tuned)
Framework: TensorFlow / Keras
📝 Project Overview
As Generative AI becomes mainstream, "virtual staging" in real estate can mislead buyers by hiding structural flaws or fabricating non-existent features.
This project provides a Forensic Signal Detector capable of identifying GenAI-generated or heavily manipulated images in high-stakes public domains. It focuses on detecting:
- Texture Consistency: "Unnatural smoothness" in walls/skin.
- Compression Discrepancies: Digital splicing of objects.
- Frequency Domain Artifacts: Mathematical fingerprints left by GANs/Diffusion models.
📊 Evaluation Results
The model was trained on a dynamic stream of Places365 images with synthetic artifact injection.
| Metric | Best Value | Epoch Achieved |
|---|
| Training Accuracy | 99.07% | Epoch 1 |
| Training Loss | 0.0201 | Epoch 1 |
| Validation Stability | 97.34% | Epoch 5 |
📦 Repository Contents
| File | Description |
|---|
forensic_model.h5 | The Model. Fine-tuned EfficientNetB0 weights. |
predict.py | Inference Script. Automates the detection process for a folder of images. |
hackathon_test_set.zip | The Dataset. Contains 1,000 images (500 Real / 500 Fake) for testing. |
requirements.txt | Dependencies. Required Python libraries. |
💻 How to Run Inference
1. Installation
Clone the repository and install dependencies:
1git clone [https://huggingface.co/](https://huggingface.co/)[FatimahEmadEldin]/{GenAI-RealEstate-Detector}
2cd {GenAI-RealEstate-Detector}
3pip install -r requirements.txt
4
2. Run Prediction
You can run the detection script on any folder of images:
1python predict.py --input_dir ./path_to_images --output_file predictions.json
2
3. Output Format
The system produces a JSON report with authenticity scores and VLM-based reasoning:
1{{
2 "image_name": "living_room_01.jpg",
3 "authenticity_score": 0.99,
4 "manipulation_type": "Synthetic/Inpainting",
5 "vlm_reasoning": "Forensic analysis detected high-frequency artifacts (0.99). Visual context suggests issues near: window reflection."
6}}
7
🧠 Training Strategy: "Self-Supervised Artifact Injection"
Instead of relying on a static dataset of "Fake" images (which may become outdated), we implemented an On-the-Fly Artifact Generator that corrupts real images (from Places365) during training.
The 3 Forensic Signals:
- Simulating Upscalers: Applied randomized Bi-cubic downsampling + Gaussian Blur.
- Simulating Diffusion: Injected High-Frequency Gaussian Noise distributions.
- Simulating GANs: Injected subtle Checkerboard Grid patterns (frequency domain fingerprints).
📄 License
This project uses the Places365 dataset (MIT CSAIL). The code is open-source under the MIT License.
