crmark 0.0.4

A robust reversible watermarking method that can robustly extract the watermark in lossy channels and perfectly recover both the cover image and the watermark in lossless channels.

📝 Introduction

CRMark: Cover-Recoverable Watermark, a robust and reversible invisible image watermarking method. CRMark enables perfect reconstruction of the original cover image in lossless channels and robust watermark extraction in lossy channels.

CRMark leverages an Integer Invertible Watermark Network (iIWN) to achieve lossless and invertible mapping between cover-watermark pairs and stego images. It addresses the trade-off between robustness and reversibility in traditional robust reversible watermarking methods, offering significant improvements in robustness, visual quality, and computational efficiency.

Key features:

• Robustness: Enhanced against distortions through an Encoder-Noise Layer-Decoder framework.
• Reversibility: Ensures lossless recovery of both the cover image and the watermark in lossless channel.
• Efficiency: Reduces time complexity and auxiliary bitstream length.

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🚀 Usage

pip install crmark

code

import os
import random
import string
import numpy as np
from PIL import Image
from crmark import CRMark

# Create output directory if not exists
os.makedirs("images", exist_ok=True)

# Initialize CRMark in color mode
crmark = CRMark(model_mode="color_256_100", float64=False)


# Generate a random string of length 3 (total 24 bits)
def generate_random_string(n: int) -> str:
    characters = string.ascii_letters + string.digits
    return ''.join(random.choices(characters, k=n))


# Random string message
str_data = generate_random_string(7)
print(str_data)

# Define image paths
cover_path = "images/color_cover.png"
rec_cover_path = "images/rec_color_cover.png"
stego_path_clean = "images/color_stego_clean.png"
stego_path_attacked = "images/color_stego_attacked.png"

# === Case 1: Without attack ===
# Encode string into image
cover_image = np.float32(Image.open(cover_path))
success, stego_image = crmark.encode(cover_image, str_data)
stego_image.save(stego_path_clean)

# Recover cover and message from clean image
stego_clean_image = np.float32(Image.open(stego_path_clean))
is_attacked_clean, rec_cover_clean, rec_message_clean = crmark.recover(stego_clean_image)
is_decoded, extracted_message_clean = crmark.decode(stego_clean_image)
rec_cover_clean.save(rec_cover_path)

# Compute pixel difference between original and recovered cover
cover = np.float32(Image.open(cover_path))
rec_clean = np.float32(rec_cover_clean)
diff_clean = np.sum(np.abs(cover - rec_clean))

# === Case 2: With attack ===
# Slightly modify the image to simulate attack
stego = np.float32(Image.open(stego_path_clean))
H, W, C = stego.shape
rand_y = random.randint(0, H - 1)
rand_x = random.randint(0, W - 1)
rand_c = random.randint(0, C - 1)

# Apply a small perturbation (±1)
perturbation = random.choice([-1, 1])
stego[rand_y, rand_x, rand_c] = np.clip(stego[rand_y, rand_x, rand_c] + perturbation, 0, 255)
Image.fromarray(np.uint8(stego)).save(stego_path_attacked)

# Recover from attacked image
stego_attacked_image = np.float32(Image.open(stego_path_attacked))
is_attacked, rec_cover_attacked, rec_message_attacked = crmark.recover(stego_attacked_image)
is_attacked_flag, extracted_message_attacked = crmark.decode(stego_attacked_image)

rec_attacked = np.float32(rec_cover_attacked)
diff_attacked = np.sum(np.abs(cover - rec_attacked))

# === Print results ===
print("=== Without Attack ===")
print("Original Message:", str_data)
print("Recovered Message:", rec_message_clean)
print("Extracted Message:", extracted_message_clean)
print("Is Attacked:", is_attacked_clean)
print("L1 Pixel Difference:", diff_clean)

print("\n=== With Attack ===")
print("Recovered Message:", rec_message_attacked)
print("Extracted Message:", extracted_message_attacked)
print("Is Attacked:", is_attacked)
print("L1 Pixel Difference:", diff_attacked)