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# demo_script.py
import tensorflow as tf
from watermarking_functions import detect_watermark_LSB
# Load the trained model with the embedded watermark
model_with_watermark = tf.keras.models.load_model('text_classification_model_with_watermark.h5')
# Detect and extract the watermark from the model
detected_watermark = detect_watermark_LSB(model_with_watermark)
if detected_watermark:
print("Watermark Detected:", detected_watermark)
else:
print("No watermark found or watermark detection failed.") |