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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
# Load model
print("Loading DeepGuard model...", flush=True)
model = tf.keras.models.load_model("deepfake_model.keras")
print("Model loaded successfully!", flush=True)
def generate_fft(image):
"""Generate FFT magnitude spectrum visualization."""
try:
img_gray = image.convert('L').resize((128, 128))
img_array = np.array(img_gray, dtype=np.float32)
f_transform = np.fft.fft2(img_array)
f_shift = np.fft.fftshift(f_transform)
magnitude = np.abs(f_shift)
magnitude = np.log1p(magnitude)
magnitude = ((magnitude - magnitude.min()) / (magnitude.max() - magnitude.min()) * 255).astype(np.uint8)
return Image.fromarray(magnitude)
except Exception as e:
print(f"FFT Error: {e}")
return None
def predict(image):
"""Main prediction function."""
if image is None:
return "Please upload an image", None
try:
img = image.convert('RGB').resize((128, 128))
img_array = np.array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
prediction = float(model.predict(img_array, verbose=0)[0][0])
if prediction > 0.5:
label = "FAKE (AI Generated)"
confidence = prediction * 100
else:
label = "REAL (Authentic)"
confidence = (1 - prediction) * 100
result_text = f"""## Detection Result: {label}
**Confidence:** {confidence:.2f}%
**Raw Model Score:** {prediction:.6f}
---
### Interpretation
- Score > 0.5: Model detects GAN artifacts = FAKE
- Score < 0.5: No GAN artifacts detected = REAL
### Important Note
This model is trained on StyleGAN-generated faces and may not accurately detect images from modern diffusion models (Stable Diffusion, Midjourney, DALL-E).
"""
fft_image = generate_fft(image)
return result_text, fft_image
except Exception as e:
return f"Error: {str(e)}", None
# Simple Interface
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", label="Upload Face Image"),
outputs=[
gr.Markdown(label="Detection Result"),
gr.Image(label="FFT Frequency Analysis")
],
title="DeepGuard - AI Face Authenticator",
description="Detect AI-generated (deepfake) faces using deep learning. Upload a face image for analysis.",
article="""
### Model Info
- **Architecture:** XceptionTransfer
- **Training Data:** 140,000 real and GAN-generated faces
- **Accuracy:** 88% on test dataset
### FFT Interpretation
| Pattern | Meaning |
|---------|---------|
| Bright center | Normal low-frequency content |
| Radiating spokes | Edge directions in image |
| Grid artifacts | Potential GAN fingerprint |
### Limitations
This model is trained on StyleGAN faces only. It may not detect Stable Diffusion, Midjourney, or DALL-E images.
""",
allow_flagging="never"
)
if __name__ == "__main__":
demo.launch()
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