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# watermarking_functions.py
import numpy as np
import hashlib
import random
import secrets
# 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.
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.
Returns:
model : object
The model with the embedded watermark.
"""
# Convert watermark data to bytes
watermark_bytes = watermark_data.encode('utf-8')
# 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")
# Flatten and concatenate all model parameters
flattened_weights = np.concatenate([w.flatten() for w in model.get_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)
# 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()])])
return model
# 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.
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 |