Update watermark_function.py

watermark_function.py

@@ -1,50 +1,78 @@
-# watermark_detection.py
+# watermarking_functions.py
 
-# Import necessary libraries
 import numpy as np
-import tensorflow as tf  # Assuming TensorFlow is used
+import hashlib
+import random
+import secrets
 
-# Function to detect and extract watermark from the model
-def detect_watermark(model, test_data):
-    # Load the model (assuming 'model' is the loaded model)
+# Function to embed a watermark into the model using LSB technique
+def embed_watermark_LSB(model, watermark_data):
+    """
+    Embeds a watermark into the provided model using Least Significant Bit (LSB) technique.
 
-    # Example pseudocode to demonstrate watermark detection
-    watermark_detected = False
-    watermark = None
+    Arguments:
+    model : object
+        The machine learning model object (e.g., TensorFlow/Keras model).
+    watermark_data : str
+        The watermark data to be embedded into the model.
 
-    # Check specific layers or parameters that might contain the watermark
-    # For example, if the watermark was embedded in certain weights or biases
+    Returns:
+    model : object
+        The model with the embedded watermark.
+    """
 
-    # Access a specific layer (example: last layer)
-    watermark_layer = model.layers[-1]  # Accessing the last layer as an example
+    # Convert watermark data to bytes
+    watermark_bytes = watermark_data.encode('utf-8')
 
-    # Get the weights of the layer
-    layer_weights = watermark_layer.get_weights()
+    # Ensure the watermark is within the capacity of the model parameters
+    total_capacity = sum([np.prod(w.shape) for w in model.get_weights()])
+    required_capacity = len(watermark_bytes) * 8  # 8 bits per byte
+    if required_capacity > total_capacity:
+        raise ValueError("Watermark size exceeds model capacity")
 
-    # 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
+    # Flatten and concatenate all model parameters
+    flattened_weights = np.concatenate([w.flatten() for w in model.get_weights()])
 
-    # 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!"
+    # Embed watermark bits into the least significant bits of model parameters
+    watermark_bits = ''.join(format(byte, '08b') for byte in watermark_bytes)
+    watermark_bits += '1'  # Adding stop bit
+    for i, bit in enumerate(watermark_bits):
+        flattened_weights[i] = (flattened_weights[i] & ~1) | int(bit)
 
-    return watermark_detected, watermark
+    # Reshape and update model parameters with embedded watermark
+    updated_weights = np.split(flattened_weights, [np.prod(w.shape) for w in model.get_weights()])
+    model.set_weights([w.reshape(s) for w, s in zip(updated_weights, [w.shape for w in model.get_weights()])])
 
-# 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
+    return model
 
-    # Call the watermark detection function with your loaded model and test data
-    detected, extracted_watermark = detect_watermark(model, test_data)
+# Function to detect and extract the watermark from the model using LSB detection
+def detect_watermark_LSB(model):
+    """
+    Detects and extracts the watermark from the provided model using Least Significant Bit (LSB) technique.
 
-    # Print detection results
-    print("Watermark Detected:", detected)
-    if detected:
-        print("Extracted Watermark:", extracted_watermark)
+    Arguments:
+    model : object
+        The machine learning model object (e.g., TensorFlow/Keras model).
+
+    Returns:
+    detected_watermark : str or None
+        Extracted watermark if detected, else None.
+    """
+
+    # Flatten and concatenate all model parameters
+    flattened_weights = np.concatenate([w.flatten() for w in model.get_weights()])
+
+    # Extract watermark bits from the least significant bits of model parameters
+    watermark_bits = ''
+    stop_bit = '1'
+    for bit in flattened_weights:
+        bit = int(bit) & 1
+        watermark_bits += str(bit)
+        if watermark_bits.endswith(stop_bit):
+            break
+
+    # Convert extracted bits to bytes and decode watermark
+    watermark_bytes = [int(watermark_bits[i:i+8], 2) for i in range(0, len(watermark_bits), 8)]
+    detected_watermark = bytearray(watermark_bytes).decode('utf-8')
+
+    return detected_watermark