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# watermark_detection.py
# Import necessary libraries
import numpy as np
import tensorflow as tf # Assuming TensorFlow is used
# Function to detect and extract watermark from the model
def detect_watermark(model, test_data):
# Load the model (assuming 'model' is the loaded model)
# Example pseudocode to demonstrate watermark detection
watermark_detected = False
watermark = None
# Check specific layers or parameters that might contain the watermark
# For example, if the watermark was embedded in certain weights or biases
# Access a specific layer (example: last layer)
watermark_layer = model.layers[-1] # Accessing the last layer as an example
# Get the weights of the layer
layer_weights = watermark_layer.get_weights()
# Analyze the weights or specific parameters for watermark presence
# Example: Check if the weights contain a specific pattern or information
# Note: This logic depends on the method used for watermark embedding
# Here, assuming watermark is embedded as a specific value in weights
watermark_value = 1.0 # Example watermark value
# Extract the watermark if the pattern or value is detected in the weights
if watermark_value in layer_weights[0]: # Considering only the first weight matrix for simplicity
watermark_detected = True
watermark = "Watermark detected in layer weights!"
return watermark_detected, watermark
# Example usage
if __name__ == "__main__":
# Load your trained model and test data
# Example: Load model and test data
model = tf.keras.models.load_model('path_to_your_model')
test_data = np.random.random((100, 10)) # Example test data
# Call the watermark detection function with your loaded model and test data
detected, extracted_watermark = detect_watermark(model, test_data)
# Print detection results
print("Watermark Detected:", detected)
if detected:
print("Extracted Watermark:", extracted_watermark) |