🔍 DetectAi

The Human Standard

Enterprise-grade, multi-modal AI content detection engine.
Catch ChatGPT essays, Midjourney images, Sora deepfakes, and ElevenLabs voice clones — all in one platform.

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🎯 The Problem

AI-generated content is everywhere — and existing detection tools are fragmented. Teachers juggle separate tools for text, images, and video. Students get falsely flagged. Deepfakes go undetected. There is no single platform that handles all modalities.

DetectAi solves this with a unified, multi-modal detection engine that analyzes text, images, video, and audio through a single API and dashboard.

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🏗️ Architecture

DetectAi uses a Branch-and-Fuse architecture. Each media type is processed through an independent pipeline composed of specialized analysis branches. Branch outputs are fused into a single, calibrated confidence score.

graph TB
    subgraph Client["🖥️ Frontend — React + Vite"]
        UI[Landing Page / Dashboard]
        Auth[Clerk Auth]
        SEO[Programmatic SEO Engine]
    end

    subgraph Gateway["⚡ FastAPI Gateway — Azure App Service"]
        direction TB
        TextRouter["/detect/text"]
        ImageRouter["/detect/image"]
        VideoRouter["/detect/video"]
        AudioRouter["/detect/audio"]
    end

    subgraph TextEngine["📝 Text Engine — 5 Branches"]
        T1[RoBERTa Transformer]
        T2[GPT-2 Perplexity]
        T3[Stylometry Analysis]
        T4[Sentence-BERT Embeddings]
        T5[Humanization Detector]
        TF[LightGBM Meta-Classifier]
    end

    subgraph ImageEngine["🖼️ Image Engine — 6 Branches"]
        I1[ViT Deep Classifier]
        I2[Error Level Analysis]
        I3[FFT Spectral Analysis]
        I4[EXIF Metadata Inspection]
        I5[PRNU Sensor Noise]
        I6[Statistical Heuristics]
        IF[Weighted Confidence Fusion]
    end

    subgraph VideoEngine["🎬 Video Engine — 9 Branches"]
        V1[ViT + DinoV2 Ensemble]
        V2[Temporal Consistency]
        V3[RAFT Optical Flow]
        V4[MiDaS Depth Physics]
        V5[CLIP Semantic Drift]
        V6[FFT Spectral Score]
        V7[PRNU Sensor Noise]
        V8[Audio-Visual Sync]
        V9[Codec Heuristics]
        VF[9-Branch Weighted Fusion]
    end

    subgraph AudioEngine["🎙️ Audio Engine"]
        A1[MFCC + Spectral Features]
        A2[Calibrated Random Forest]
    end

    UI -->|HTTPS| Gateway
    TextRouter --> TextEngine
    ImageRouter --> ImageEngine
    VideoRouter --> VideoEngine
    AudioRouter --> AudioEngine

    T1 & T2 & T3 & T4 & T5 --> TF
    I1 & I2 & I3 & I4 & I5 & I6 --> IF
    V1 & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 --> VF
    A1 --> A2

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📖 Deep dive: See docs/architecture.md for full technical details on each branch.

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✨ Features

Feature	Description
🔤 Text Detection	5-branch ensemble (RoBERTa, perplexity, stylometry, embeddings, humanization) with LightGBM meta-classifier and per-sentence explainability
🖼️ Image Detection	6-branch fusion (ViT, ELA, FFT, EXIF, PRNU, heuristics) with perceptual hash caching
🎬 Video Detection	9-branch fusion (ViT, DinoV2, RAFT optical flow, MiDaS depth, CLIP semantic drift, temporal analysis)
🎙️ Audio Detection	Spectral feature extraction + calibrated Random Forest for voice clone detection
📊 Dashboard	Real-time scan results, history tracking, analytics charts
🔐 Authentication	Clerk-powered OAuth with SSO support
🌗 Dark Mode	Full dark/light theme support with smooth transitions
🔍 SEO Engine	Programmatic long-tail SEO with Schema.org structured data and auto-generated sitemap

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🛠️ Tech Stack

Layer	Technology
Frontend	React 19, Vite, Framer Motion, Recharts
Auth	Clerk
Backend	FastAPI, Python 3.11, Uvicorn
ML Models	PyTorch, HuggingFace Transformers (ViT, RoBERTa, DinoV2, RAFT, MiDaS, GPT-2)
Ensemble	LightGBM, scikit-learn (isotonic calibration)
Task Queue	Celery + Redis
Database	SQLite
Hosting	Azure App Service (B3), Vercel
CI/CD	GitHub Actions → Azure auto-deploy
Analytics	Vercel Web Analytics

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📁 Project Structure

Click to expand directory tree

detectai/
├── backend/                    # FastAPI application
│   ├── main.py                 # App entrypoint & router registration
│   ├── text/                   # Text detection module
│   │   ├── router.py           # /detect/text endpoint
│   │   ├── engines/            # 5 detection branches
│   │   │   ├── transformer.py  #   └─ RoBERTa classifier
│   │   │   ├── perplexity.py   #   └─ GPT-2 perplexity scorer
│   │   │   ├── stylometry.py   #   └─ Statistical text analysis
│   │   │   ├── embeddings.py   #   └─ Sentence-BERT similarity
│   │   │   └── humanization.py #   └─ Paraphrase detector
│   │   ├── ensemble/           # Meta-classifier & calibration
│   │   ├── explainability/     # Per-sentence attribution
│   │   ├── preprocessors/      # Chunking & language detection
│   │   └── models/             # Serialized model artifacts
│   ├── image/                  # Image detection module
│   │   ├── router.py           # /detect/image endpoint
│   │   ├── fusion.py           # Weighted branch fusion
│   │   ├── tasks.py            # Celery async tasks
│   │   └── branches/           # 6 detection branches
│   │       ├── deep_classifier.py  # ViT classifier
│   │       ├── ela.py              # Error Level Analysis
│   │       ├── fft_cnn.py          # FFT spectral analysis
│   │       ├── metadata.py         # EXIF inspection
│   │       ├── prnu_residual.py    # Sensor noise analysis
│   │       └── advanced_heuristics.py
│   ├── video/                  # Video detection module
│   │   ├── router.py           # /detect/video endpoint
│   │   └── branches/           # 9 detection branches
│   │       ├── vit_ensemble.py     # ViT + DinoV2
│   │       ├── temporal_consistency.py
│   │       ├── motion_physics.py   # RAFT optical flow
│   │       ├── depth_physics.py    # MiDaS depth estimation
│   │       ├── semantic_drift.py   # CLIP embeddings
│   │       ├── fft_score.py
│   │       ├── prnu_score.py
│   │       ├── audio_physics.py
│   │       └── advanced_heuristics.py
│   ├── audio/                  # Audio detection module
│   │   ├── router.py           # /detect/audio endpoint
│   │   └── tasks.py            # Feature extraction & classification
│   ├── database.py             # SQLite connection manager
│   ├── history.py              # Scan history CRUD
│   ├── models.py               # Pydantic schemas
│   ├── celery_app.py           # Task queue config
│   ├── Dockerfile              # Container build
│   └── requirements.txt        # Python dependencies
├── frontend/                   # React SPA
│   ├── src/
│   │   ├── App.jsx             # Router & providers
│   │   ├── pages/              # Page components
│   │   ├── components/         # Shared UI components
│   │   ├── config/seoConfig.js # Programmatic SEO configuration
│   │   ├── context/            # Theme provider
│   │   └── lib/                # Utility functions
│   ├── public/                 # Static assets, sitemap, robots.txt
│   ├── index.html              # HTML entry point
│   └── vite.config.js          # Vite build configuration
├── docs/                       # Technical documentation
│   ├── architecture.md         # System architecture deep-dive
│   ├── api_reference.md        # API endpoint reference
│   └── text_engine_architecture.md
├── .github/workflows/          # CI/CD pipelines
│   └── azure-deploy.yml        # Auto-deploy to Azure on push
├── .gitignore
├── CONTRIBUTING.md
├── LICENSE                     # All Rights Reserved
└── README.md                   # ← You are here

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🚀 Getting Started

Prerequisites

• Python 3.11+
• Node.js 18+
• Git

1. Clone the Repository

git clone https://github.com/Harsh2o/detectai.git
cd detectai

2. Backend Setup

cd backend
python -m venv venv
source venv/bin/activate        # Windows: venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env            # Add your HuggingFace token
uvicorn main:app --reload --host 0.0.0.0 --port 8000

The API will be live at http://localhost:8000/docs

3. Frontend Setup

cd frontend
npm install
cp .env.example .env.local      # Configure API URL & Clerk keys
npm run dev

The app will be live at http://localhost:5173

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🌐 Deployment

Backend → Azure App Service

The backend auto-deploys via GitHub Actions. Every push to main triggers the workflow in .github/workflows/azure-deploy.yml.

# Deploys to: https://detectai-api-xxxxx.azurewebsites.net

Frontend → Vercel

The frontend auto-deploys via Vercel's GitHub integration. Connect your repo in the Vercel dashboard and set the following environment variables:

Variable	Value
VITE_API_URL	Your Azure backend URL
VITE_CLERK_PUBLISHABLE_KEY	Your Clerk publishable key

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📖 Documentation

Document	Description
Architecture	Full system architecture, engine details, and infrastructure
API Reference	Complete REST API endpoint documentation
Text Engine	Deep-dive into the text detection pipeline
Contributing	Development setup and contribution guidelines

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📄 License

This project is licensed under All Rights Reserved — see the LICENSE file for details. You may view this code for educational purposes, but copying, deploying, or modifying it is strictly prohibited.

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Built with ❤️ by Harsh Gupta

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