crmark 1.1.1

📝 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.

🚀 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_64_complex")


# 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))


# Calculate PSNR between two images
def calculate_psnr(img1, img2):
    mse = np.mean((img1 - img2) ** 2)
    if mse == 0:
        return float('inf')
    PIXEL_MAX = 255.0
    psnr = 20 * np.log10(PIXEL_MAX / np.sqrt(mse))
    return psnr


# Random string message
str_data = generate_random_string(5)
str_data = "hello"
print(str_data)

# Define image paths
 cover_path = "images/cover_color.png"
rec_cover_path = "images/rec_color_cover.png"
attack_rec_cover_path = "images/attack_rec_color_cover.png"
stego_path_clean = "images/color_stego_clean.png"
stego_path_attacked = "images/color_stego_attacked.png"
residual_path_clean = "images/color_residual.png"

# === Case 1: Without attack ===
# Encode string into image
cover_image = np.float32(Image.open(cover_path).resize((256, 256)))
success, stego_image = crmark.encode(cover_image, str_data)
print("psnr:", calculate_psnr(np.float32(stego_image), cover_image))

if success:
    stego_image.save(stego_path_clean)
    stego_clean_image = np.float32(stego_image)
    residual = np.abs((stego_clean_image - cover_image) * 10.) + 127.5
    Image.fromarray(np.uint8(np.clip(residual, 0, 255))).save(residual_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).resize((256, 256)))
    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)

    stego = np.clip(stego + np.random.randint(0, 20, size=stego.shape), 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_cover_attacked.save(attack_rec_cover_path)

    rec_attacked = np.float32(rec_cover_attacked)
    diff_attacked = np.sum(np.abs(cover - rec_attacked))
    attack_rec_psnr = calculate_psnr(rec_attacked, cover)
    # === Print results ===
    print("=== Without Attack ===")
    print("Original Message:", str_data)
    print("Recovered Message:", rec_message_clean)
    print("Extracted Message:", extracted_message_clean)
    print("Was 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("Was Attacked:", is_attacked)
    print("L1 Pixel Difference:", diff_attacked)
    print("L1 Pixel Psnr:", attack_rec_psnr)

else:
    print("Emebdding failed!")