DeepGuard AI — Deepfake Detection Project

A deep learning deepfake detection system built with EfficientNetV2B0 + CBAM Attention, available as both a Streamlit web app and a Chrome Extension for real-time AI-generated content detection on any web page.

🛡️ Chrome Extension (DeepGuard AI)

Features

• Real-time page scanning — Automatically detects AI-generated images and videos on any web page
• 100% local inference — All analysis runs on-device using TensorFlow.js; no data ever leaves your machine
• Smart detection — MutationObserver-based scanning catches dynamically loaded content (infinite scroll, SPAs)
• Video frame analysis — Extracts and analyzes video frames at configurable intervals
• Premium UI — Glassmorphism popup dashboard with real-time stats, detection history, and configurable settings
• In-page overlays — Non-intrusive badges on scanned images showing Real/AI-Generated status with confidence scores
• User-configurable — Toggle auto-scan, adjust sensitivity, show/hide overlays, enable/disable video scanning

Extension Setup

1. Convert the trained model to TensorFlow.js format:

   pip install tensorflowjs
   python scripts/convert_model.py

   This produces extension/model/model.json + weight .bin files.

2. Load the extension in Chrome:

   • Open chrome://extensions/
   • Enable Developer mode (top-right toggle)
   • Click Load unpacked
   • Select the extension/ folder

3. Usage:

   • Click the 🛡️ DeepGuard AI icon in the toolbar
   • Click "Scan This Page" to analyze all images/videos
   • Toggle Auto-Scan in settings to scan pages automatically
   • Hover over badges on images to see detailed confidence scores

Extension Architecture

extension/
├── manifest.json          # Manifest V3 configuration
├── background.js          # Service worker (message routing, state management)
├── content/
│   ├── content.js         # DOM scanner, MutationObserver, overlay injection
│   └── overlay.css        # In-page glassmorphism badges
├── offscreen/
│   ├── offscreen.html     # Hidden document for TF.js inference
│   └── offscreen.js       # Model loading, preprocessing, prediction
├── popup/
│   ├── popup.html         # Dashboard UI
│   ├── popup.css          # Premium glassmorphism styles
│   └── popup.js           # Settings, stats, scan control
├── lib/
│   └── tf.min.js          # TensorFlow.js library (~1.4 MB)
├── model/
│   ├── model.json         # Converted model topology
│   └── *.bin              # Weight shards (~10-15 MB)
└── assets/
    └── icons/             # Extension icons (16, 48, 128px)

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🔬 ML Training Pipeline (Python)

Folder Structure

• data/raw: Original dataset images/videos (managed by download_data.py).
• data/processed: Cropped faces and preprocessed data.
• models/: Saved model weights (.h5, .keras).
• src/: Source code for preprocessing, training, and inference.
• notebooks/: Jupyter notebooks for experimentation.
• scripts/: Model conversion utilities.
• download_data.py: Script to download and set up the dataset.

Environment Setup

1. Create a virtual environment:

   python -m venv venv

2. Activate the environment:
   • Windows: .\venv\Scripts\activate
   • Mac/Linux: source venv/bin/activate
3. Install dependencies:

   pip install -r requirements.txt

Dataset Setup

1. Kaggle API:
   • Go to Kaggle Account.
   • Click "Create New API Token" to download kaggle.json.
   • Place kaggle.json in C:\Users\<YourUser>\.kaggle\ OR in this project folder.
2. Download Data:

   python download_data.py

   This will download the ciplab/real-and-fake-face-detection dataset and organize it into data/raw/real and data/raw/fake.

Data Preprocessing

Run the preprocessing script to extract faces from videos/images:

python src/preprocess.py

This will populate data/processed/real and data/processed/fake.

Model Training

Train the EfficientNetV2B0 + CBAM model with two-phase training:

python src/train.py

This saves the model to models/deepfake_detector.keras.

Evaluation

Generate a comprehensive evaluation report:

python src/evaluate.py

Streamlit App

Launch the web interface for manual video analysis:

streamlit run app.py

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Model Architecture

• Base Model: EfficientNetV2B0 (pre-trained on ImageNet)
• Attention: CBAM (Convolutional Block Attention Module)
• Custom Head: GAP → Dense(512) → BN → Dropout → Dense(256) → BN → Dropout → Sigmoid
• Training: Two-phase (frozen backbone → fine-tuning top 30 layers)
• Loss: Binary Focal Crossentropy
• Metrics: Accuracy, AUC, Precision, Recall

Requirements

• Python 3.10+
• TensorFlow 2.12+
• Chrome 120+ (for extension)
• See requirements.txt for full dependency list