README.md

metadata

title: FakeShield API
emoji: 🛡️
colorFrom: indigo
colorTo: blue
sdk: docker
app_port: 7860
pinned: false

🛡️ FakeShield: AI Forensic Laboratory

FakeShield is a state-of-the-art, multi-modal deepfake detection platform designed for researchers, journalists, and security professionals. It leverages advanced machine learning ensembles to detect AI-generated content across Text, Image, Audio, and Video with surgical precision.

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🚀 Key Features

• Multimodal Analysis: Four dedicated forensic labs for different media types.
• Explainable AI (XAI): Provides sentence-level highlighting and heatmap overlays.
• Vanguard Engine: A proprietary ensemble (RoBERTa + GPT2 + Binoculars) for high-accuracy text detection.
• Real-time Processing: Fast inference with background warmup for zero-latency analysis.
• Enterprise Dashboard: Unified view for history, statistics, and lab management.

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🏗️ System Architecture

graph TD
    User((User)) -->|Uploads Media| Frontend[React Dashboard]
    Frontend -->|API Request| Gateway[FastAPI Backend]
    Gateway -->|Authentication| DB[(MongoDB Atlas)]
    
    subgraph Forensic Engines
        Gateway --> TextLab[Vanguard Text Engine]
        Gateway --> ImageLab[Image Forensic Suite]
        Gateway --> AudioLab[Audio Deepfake Lab]
        Gateway --> VideoLab[Video Consistency Lab]
    end
    
    TextLab -->|Results| Frontend
    ImageLab -->|Heatmaps| Frontend
    AudioLab -->|Spectrograms| Frontend
    VideoLab -->|Frame Analysis| Frontend

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🧪 Forensic Labs in Detail

1. Text Forensic Lab (Vanguard v60.0)

The Text Lab uses the Vanguard Engine, a 3-layer ensemble designed to bypass "humanized" AI text.

How it works:

1. Neural Signature: Uses RoBERTa-HC3 to identify architectural patterns common in LLMs.
2. Statistical Signal: Measures Perplexity and Burstiness using GPT2-Medium to detect "flat" linguistic entropy.
3. Zero-Shot Profiling: Employs Binoculars (Observer vs Performer ratio) for high-confidence classification without specific training.

graph LR
    Input[Raw Text] --> Pre[Pre-processing & Tokenization]
    Pre --> R[RoBERTa Neural Match]
    Pre --> G[GPT2 Statistical Signal]
    Pre --> B[Binoculars Zero-Shot]
    R & G & B --> Fusion[Ensemble Decision Engine]
    Fusion --> Judge[Gemini AI Logic Check]
    Judge --> Result[Final Verdict & Heatmap]

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2. Image Forensic Lab

Analyzes images for manipulated pixels and metadata inconsistencies.

Forensic Layers:

• ELA (Error Level Analysis): Identifies different compression levels indicating local edits.
• DINOv2 Heatmaps: Uses Vision Transformers to find semantic inconsistencies in textures.
• PRNU (Photo Response Non-Uniformity): Detects "sensor fingerprints" to verify camera authenticity.

graph TD
    Img[Input Image] --> ELA[Error Level Analysis]
    Img --> ViT[DINOv2 Semantic Check]
    Img --> Meta[Metadata/C2PA Audit]
    ELA --> Result[Artifact Visualization]
    ViT --> Result
    Meta --> Result

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3. Audio Forensic Lab

Detects voice cloning and synthetic speech patterns.

Forensic Layers:

• WavLM Integration: Analyzes speech representations to find synthetic artifacts.
• Spectral Variance: Detects the "robotic" consistency of AI-generated voices.
• Speaker Consistency: Verifies if the voice signature remains stable throughout the clip.

graph LR
    Audio[Audio Clip] --> Spec[Spectrogram Generation]
    Spec --> WavLM[Feature Extraction]
    Spec --> Stat[Acoustic Statistical Analysis]
    WavLM & Stat --> Detector[Synthetic Voice Matcher]
    Detector --> Verdict[Authentic vs Synthetic]

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4. Video Forensic Lab

Detects deepfake faces and temporal inconsistencies in video streams.

Forensic Layers:

• Face Consistency: Checks for frame-to-frame jitter in facial landmarks.
• Lip-Sync Audit: Cross-references audio signals with lip movements.
• Temporal Artifacts: Identifies "ghosting" or blending issues in video frames.

graph TD
    Video[Video File] --> Frames[Frame Extraction]
    Frames --> Face[Facial Landmark Tracking]
    Frames --> Temp[Temporal Smoothing Check]
    Face --> Consist[Consistency Score]
    Temp --> Consist
    Consist --> Final[Deepfake Detection Score]

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🛠️ Technology Stack

• Frontend: React 18, Vite, TypeScript, Tailwind CSS, Framer Motion, Lucide Icons.
• Backend: FastAPI, Python 3.10, Uvicorn.
• ML/AI: PyTorch, Transformers (Hugging Face), Optimum (ONNX), OpenCV, Librosa.
• Database: MongoDB Atlas (NoSQL).
• Deployment: Vercel (Frontend) & Hugging Face Spaces (Backend).

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📦 Installation & Setup

Prerequisites

• Python 3.10+
• Node.js 18+
• MongoDB Instance

Local Development

1. Clone the Repo:

   git clone https://github.com/Akash4782/Fakeshield.git
   cd Fakeshield
   

2. Backend Setup:

   cd backend
   python -m venv .venv
   source .venv/bin/activate # Windows: .venv\Scripts\activate
   pip install -r requirements.txt
   python start_backend.py
   

3. Frontend Setup:

   cd fakeshield
   npm install
   npm run dev
   

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🛡️ License

Distributed under the MIT License. See LICENSE for more information.

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Created with ❤️ by Akash Virdi as a Final Year Project.