| ---
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| title: Coin Toss Detector
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| emoji: 🪙
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| colorFrom: blue
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| colorTo: yellow
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| sdk: docker
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| app_port: 7860
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| ---
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|
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| # COIN-TOSS: AI & Identity Risk Detection
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|
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| ## Overview
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| 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.
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|
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| ## Features
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|
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| - **High-Accuracy AI Detection**:
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| - Utilizes a hybrid ensemble of models (`dima806/ai_vs_real_image_detection` and `prithivMLmods/Deep-Fake-Detector-v2-Model`).
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| - **Gap Trap V3 Logic**: A specialized algorithm to catch "uncanny valley" images and properly classify filtered real photos vs. high-quality deepfakes.
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| - **Frequency Analysis**: Visualizes invisible noise patterns (FFT) to detect checkerboard artifacts common in diffusion models.
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| - **Identity Theft Risk Analysis**:
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| - Analyzes "Real" images for biometric metrics (Face Visibility, Quality, etc.).
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| - Provides a risk assessment (Low/High) for using the image in sensitive contexts (KYC, Profiles).
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| - **User-Friendly Interface**:
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| - Simple drag-and-drop upload.
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| - Instant "Real" or "AI" verdict.
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| - Detailed analysis points explaining the decision.
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|
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| ## Workflow
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|
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| ### Prerequisites
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|
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| - Python 3.8+
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| - Git
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|
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| ### Installation
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| 1. **Clone the Repository**
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| ```bash
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| git clone https://github.com/madhavmullick2025/COIN-TOSS.git
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| cd COIN-TOSS
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| ```
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|
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| 2. **Install Dependencies**
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| It is recommended to use a virtual environment.
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| ```bash
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| pip install -r requirements.txt
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| ```
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|
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| ### Usage
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|
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| 1. **Start the Application**
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| ```bash
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| python app.py
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| ```
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| *Note: The first run may take a few moments to download the necessary model weights from HuggingFace.*
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|
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| 2. **Access the Interface**
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| Open your web browser and navigate to:
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| ```
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| http://localhost:5002
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| ```
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|
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| 3. **Analyze Images**
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| - Upload an image (JPG, PNG, WEBP).
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| - Click "Analyze" to see if it's Real or AI.
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| - If "Real", switch to the "Identity Risk" tab to see safety metrics.
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|
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| ## Tech Stack
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| - **Backend**: Python, Flask, PyTorch, Transformers (HuggingFace).
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| - **Frontend**: HTML5, CSS3, JavaScript.
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| - **AI Models**: ViT (Vision Transformer) based image classifiers.
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|
