How to Build an AI vs Human Image Detector Using Streamlit & Transformers

Source: Dev.to

Artificial Intelligence models like SDXL, Grok, Gemini, and others are producing images so realistic that even humans can’t always tell them apart from real photos. As these models get better, traditional detectors become less effective.

In this guide, you’ll learn how to build your own AI‑vs‑Human Image Detector using:

• Streamlit for the UI
• Hugging Face Transformers
• PyTorch

The detection model used is Organika/sdxl-detector.

Overview of What We’re Building

The detector:

• Accepts an uploaded image
• Processes it with a pretrained deep‑learning model
• Predicts whether the image is AI‑generated or Human‑captured
• Displays the model’s confidence score
• Works on CPU, CUDA, or Apple Silicon (MPS)

All of this runs inside a simple Streamlit app that can be executed locally or deployed online.

Environment Setup and Packages Installation

Create a Virtual Environment

python -m venv env
source env/bin/activate   # Linux/macOS
# env\Scripts\activate    # Windows

Install Required Packages

# Core dependencies
pip install streamlit pillow torch transformers accelerate

# Optional: downgrade NumPy if you encounter compatibility issues with PyTorch
# pip install "numpy"

Source code:
YouTube demo: