# 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) |