# 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)