🛡️ AI Detector

Unified AI-generated content detection for images, videos, and deepfake audio.

A powerful, modular Python toolkit that combines image/video AI detection (CLIP & ViT) and audio deepfake detection (spectral analysis & RandomForest) into one unified CLI and web interface.

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Warning

Detection results may not always be accurate. AI‑generated content detection is an evolving field — always verify with additional methods when necessary.

✨ Features

Module	Capabilities
🖼️ Image Detection	CLIP & ViT model classification, noise analysis, texture analysis, Fourier Transform pattern detection, metadata inspection, invisible watermark detection
🎬 Video Detection	Frame‑by‑frame analysis using the same image detection pipeline
🎵 Audio Deepfake Detection	Spectral feature extraction (MFCCs, spectral centroid, chroma, ZCR) with a RandomForest classifier
🌐 Gradio Web UI	Tabbed interface for image and audio detection — upload & analyze in your browser
⌨️ CLI	Scriptable command‑line interface for quick or batch processing

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📋 Prerequisites

Requirement	Details
Python	>= 3.11
PyTorch	>= 2.5 (CUDA‑enabled GPU recommended for faster inference)
OS	Windows / Linux / macOS

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🚀 Installation

1. Clone the repository

git clone https://github.com/iamsrishanth/AI-Detector.git
cd AI-Detector

2. Create a virtual environment (recommended)

python -m venv venv

# Windows
venv\Scripts\activate

# Linux / macOS
source venv/bin/activate

3. Install dependencies

Using pip:

pip install -e .

Using uv (fast Python package manager):

uv sync

Using requirements.txt:

pip install -r requirements.txt

Tip

If you have an NVIDIA GPU, ensure you install the CUDA‑enabled version of PyTorch for significantly faster inference. Visit pytorch.org/get-started for platform-specific instructions.

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🎯 Usage

Command‑Line Interface (CLI)

# Analyze an image
ai-detect --image path/to/image.jpg

# Analyze a video
ai-detect --video path/to/video.mp4

# Analyze an audio file
ai-detect --audio path/to/audio.wav

# Launch the Gradio web interface
ai-detect --gui

Python API

from ai_detector.image import ImageDetector
from ai_detector.audio import AudioProcessor, DeepfakeDetector

# ── Image / Video Detection ──────────────────────────────
detector = ImageDetector()
detector.load_models()
result = detector.process_image("photo.jpg")       # single image
result = detector.process_video("clip.mp4")         # video

# ── Audio Deepfake Detection ─────────────────────────────
processor = AudioProcessor()
det = DeepfakeDetector()
det.load_model()

features  = processor.extract_features("audio.wav")
result    = det.predict(features)

print(f"Deepfake probability: {result['deepfake_probability'] * 100:.1f}%")

Gradio Web UI

Launch the web interface and open it in your browser:

ai-detect --gui

The UI provides a tabbed interface with separate tabs for Image Detection and Audio Detection — simply upload a file and click Analyze.

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📂 Project Structure

AI-Detector/
├── ai_detector/
│   ├── __init__.py               # Package init
│   ├── app.py                    # Gradio web interface
│   ├── cli.py                    # CLI entry point
│   ├── image/
│   │   ├── __init__.py
│   │   └── detector.py           # Image & video detection (CLIP + ViT)
│   └── audio/
│       ├── __init__.py
│       ├── config.py             # Audio detection settings
│       ├── models.py             # Pydantic response models
│       ├── processor.py          # Audio feature extraction
│       └── detector.py           # Deepfake detection (RandomForest)
├── tests/
│   ├── test_image_detector.py
│   ├── test_audio_detector.py
│   └── test_audio_processor.py
├── pyproject.toml                # Project metadata & dependencies
├── requirements.txt              # Pip requirements
├── LICENSE                       # Apache License 2.0
└── README.md

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🛠️ Tech Stack

Deep Learning	PyTorch · Transformers (HuggingFace) · CLIP · ViT
Audio Analysis	Librosa · SoundFile · SciPy
ML	scikit‑learn (RandomForest)
Computer Vision	OpenCV · Pillow
Web UI	Gradio
Data Validation	Pydantic

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🧪 Running Tests

# Using pytest
pytest

# Verbose output
pytest -v

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🤝 Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.

1. Fork the repository
2. Create a feature branch — git checkout -b feature/amazing-feature
3. Commit your changes — git commit -m "Add amazing feature"
4. Push to your branch — git push origin feature/amazing-feature
5. Open a Pull Request

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📄 License

This project is licensed under the Apache License 2.0 — see the LICENSE file for details.

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_{Built with ❤️ by iamsrishanth}