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title: Coin Toss Detector
emoji: 🪙
colorFrom: blue
colorTo: yellow
sdk: docker
app_port: 7860
---
# COIN-TOSS: AI & Identity Risk Detection
## Overview
COIN-TOSS is an advanced web application designed to accurately detect AI-generated images and assess potential identity theft risks. By combining multiple state-of-the-art deep learning models with custom analysis logic ("Gap Trap V3"), it provides a reliable "Real" vs "AI" verdict without ambiguous percentages, while also identifying potential misuse of authentic images.
## Features
- **High-Accuracy AI Detection**:
- Utilizes a hybrid ensemble of models (`dima806/ai_vs_real_image_detection` and `prithivMLmods/Deep-Fake-Detector-v2-Model`).
- **Gap Trap V3 Logic**: A specialized algorithm to catch "uncanny valley" images and properly classify filtered real photos vs. high-quality deepfakes.
- **Frequency Analysis**: Visualizes invisible noise patterns (FFT) to detect checkerboard artifacts common in diffusion models.
- **Identity Theft Risk Analysis**:
- Analyzes "Real" images for biometric metrics (Face Visibility, Quality, etc.).
- Provides a risk assessment (Low/High) for using the image in sensitive contexts (KYC, Profiles).
- **User-Friendly Interface**:
- Simple drag-and-drop upload.
- Instant "Real" or "AI" verdict.
- Detailed analysis points explaining the decision.
## Workflow
### Prerequisites
- Python 3.8+
- Git
### Installation
1. **Clone the Repository**
```bash
git clone https://github.com/madhavmullick2025/COIN-TOSS.git
cd COIN-TOSS
```
2. **Install Dependencies**
It is recommended to use a virtual environment.
```bash
pip install -r requirements.txt
```
### Usage
1. **Start the Application**
```bash
python app.py
```
*Note: The first run may take a few moments to download the necessary model weights from HuggingFace.*
2. **Access the Interface**
Open your web browser and navigate to:
```
http://localhost:5002
```
3. **Analyze Images**
- Upload an image (JPG, PNG, WEBP).
- Click "Analyze" to see if it's Real or AI.
- If "Real", switch to the "Identity Risk" tab to see safety metrics.
## Tech Stack
- **Backend**: Python, Flask, PyTorch, Transformers (HuggingFace).
- **Frontend**: HTML5, CSS3, JavaScript.
- **AI Models**: ViT (Vision Transformer) based image classifiers.
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