自适应的图像数字水印算法Python

以下是一个基于Python的自适应图像数字水印算法的示例代码：

import cv2
import numpy as np

# 定义图像块大小和水印长度
block_size = 8
watermark_len = 32

# 读入原始图像和水印
img = cv2.imread('lena.png')
watermark = np.random.randint(0, 2, watermark_len)

# 对原始图像进行分块
h, w, c = img.shape
blocks_h = h // block_size
blocks_w = w // block_size
blocks_num = blocks_h * blocks_w
img_blocks = np.zeros((blocks_num, block_size, block_size, c), dtype=np.uint8)

for i in range(blocks_h):
    for j in range(blocks_w):
        img_blocks[i * blocks_w + j] = img[i * block_size:(i + 1) * block_size, j * block_size:(j + 1) * block_size]

# 对每个图像块进行变换和嵌入
for i in range(blocks_num):
    block = img_blocks[i].astype(np.float32)
    # 进行DCT变换
    block_dct = cv2.dct(block)
    # 获取变换系数的均值和标准差
    mean = np.mean(block_dct)
    std = np.std(block_dct)
    # 确定最佳嵌入位置
    max_psnr = 0
    best_pos = 0
    for pos in range(block_size * block_size - watermark_len):
        # 嵌入水印
        block_dct_copy = block_dct.copy().reshape((-1,))
        block_dct_copy[pos:pos + watermark_len] += (2 * watermark - 1) * std
        block_dct_copy[block_dct_copy < mean] = mean
        block_dct_copy[block_dct_copy > 255] = 255
        # 反变换得到水印图像块
        block_watermark = cv2.idct(block_dct_copy.reshape((block_size, block_size)))
        block_watermark = np.round(block_watermark).astype(np.uint8)
        # 计算PSNR
        psnr = cv2.PSNR(block, block_watermark)
        if psnr > max_psnr:
            max_psnr = psnr
            best_pos = pos
    # 嵌入水印
    block_dct_copy = block_dct.copy().reshape((-1,))
    block_dct_copy[best_pos:best_pos + watermark_len] += (2 * watermark - 1) * std
    block_dct_copy[block_dct_copy < mean] = mean
    block_dct_copy[block_dct_copy > 255] = 255
    # 反变换得到含有水印的图像块
    img_blocks[i] = cv2.idct(block_dct_copy.reshape((block_size, block_size))).astype(np.uint8)

# 合并图像块得到含有水印的图像
img_watermark = np.zeros((h, w, c), dtype=np.uint8)
for i in range(blocks_h):
    for j in range(blocks_w):
        img_watermark[i * block_size:(i + 1) * block_size, j * block_size:(j + 1) * block_size] = img_blocks[i * blocks_w + j]

# 显示原始图像和含水印的图像
cv2.imshow('Original Image', img)
cv2.imshow('Watermarked Image', img_watermark)
cv2.waitKey(0)
cv2.destroyAllWindows()

这个示例代码实现了一个简单的自适应图像数字水印算法，对每个图像块进行DCT变换，并根据变换系数的均值和标准差确定最佳嵌入位置。水印嵌入过程中使用了随机的二进制水印，并且只嵌入了32个比特。在实际应用中，可以根据需要调整水印长度和嵌入位置的确定方法。