给出图像
-
可见(Perceptible)/不可见(Inperceptible): 即人类的感知系统 (Human Visual System, HVS) 能否感知到水印的存在
-
脆弱(Fragile)/稳健(Robust): 脆弱水印可以检测出载体是否被修改，可用于完整性检验等; 稳健水印则再载体经过信号处理或恶意攻击后仍能被检测出来，可用于版权保护等。
-
非盲(Noblind)/半盲(Seminonblind)/全盲(Blind): 检测水印的过程中需要
$X_0,W$ 的参与称为非盲水印。无需$X_0$ 但需要$W$ 称为半盲水印。只需要$K$ 的称为全盲水印。 -
空域(Spatial)/变换域(Transform): 水印嵌入的位置。常见的变换域方法包括离散余弦变换 (Discrete Cosine Transform, DCT), 离散小波变换 (Discrete Wavelet Transform, DWT) 和离散傅里叶变换 (Discrete Fourier Transform, DFT)
水印可能会遭受下列的攻击
- 低通滤波 (中值、高斯、均值等)
- Gamma 矫正
- 直方图修正
- 图像压缩
- 翻转
- 剪切
- 旋转
- 尺度变换(一致/非一致)
- 随机行/列删除
- 随机几何失真(模拟打印后扫描的结果)
- 噪声攻击
-
MSE(Mean Square Error)：原水印和提取水印的均方误差。MSE越低说明我们的水印还原得越好。
$M S E = \frac { 1 } { x y } \sum _ { a = 0 } ^ { x - 1 } \sum _ { b = 0 } ^ { y - 1 } [ A ( a , b ) - K ( a , b ) ] ^ { 2 }$ -
PSNR(Peak Signal to Noise Ratio)：原始图像和加水印后图像的峰值信噪比。图像的 PSNR 越高，说明我们的水印越不容易被感知。
$PSNR=10\cdot \log_{10}\left(\frac{255}{\sqrt{MSE}}\right)$ (注：这里的$MSE$ 是原始图像和加水印后图像的$MSE$) -
SR(Similarity Factor): 另一个刻画原水印和提取水印相似度的计算方式，通常转成向量后点积求余弦相似度
$S F \left( A , A ^ { \prime } \right) = \frac { A ^ { * } A ^ { \prime } } { \sqrt { A ^ { \prime * } A ^ { \prime } } }$
-
DFT 非盲水印的嵌入
-
DWT 盲水印的嵌入
-
DCT 盲水印的嵌入
-
量化方法：直接修改为 -1/1
-
量化方法：根据 0/1 量化至奇/偶
-
量化方法：使用 0/1 伪随机序列
-
-
DWT+DCT 盲水印的嵌入
-
DWT+SVD 盲水印的嵌入
-
空域盲水印嵌入
-
混合盲水印嵌入
-
相关的稳健性测试
一个标准的二维码是 35*35 的 0-1 点阵，由于有三个角只是为了方便定位而没有包含基本信息，我们对二维码进行了进一步的压缩，每个二维码含有 897 bit 的信息。
import numpy as np
from scipy.fftpack import fft2, ifft2, dct, idct
import matplotlib.pyplot as plt
import pywt
import cv2
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
img = plt.imread('./image/origin.png')
origin_watermark = plt.imread('./image/watermark.png')
binary_watermark = np.loadtxt('./qrcode.txt', dtype=int) // 255
corner = np.zeros((9, 9), dtype=np.uint8)
corner[0, :] = 1
corner[8, :] = 1
corner[:, 0] = 1
corner[:, 8] = 1
corner[2, 2:7] = 1
corner[6, 2:7] = 1
corner[3:6, 2] = 1
corner[3:6, 6] = 1
def qrcode2series(qrcode): # thr corner in qrcord
idx = 0
series = np.zeros(897)
for i in range(1, 9):
for j in range(9, 26):
series[idx] = qrcode[i, j]
idx += 1
for i in range(9, 26):
for j in range(1, 34):
series[idx] = qrcode[i, j]
idx += 1
for i in range(26, 34):
for j in range(9, 34):
series[idx] = qrcode[i, j]
idx += 1
return series
def series2qrcode(series):
qrcode = np.ones((35, 35))
qrcode[0:9, 0:9] = corner
qrcode[0:9, 26:35] = corner
qrcode[26:35, 0:9] = corner
idx = 0
for i in range(1, 9):
for j in range(9, 26):
qrcode[i, j] = series[idx]
idx += 1
for i in range(9, 26):
for j in range(1, 34):
qrcode[i, j] = series[idx]
idx += 1
for i in range(26, 34):
for j in range(9, 34):
qrcode[i, j] = series[idx]
idx += 1
return qrcode
def show_imgs(fig, img, img_marked, img_attacked, watermark, reconstruct):
fig.add_subplot(1, 5, 1)
plt.title('marked img', fontsize=24)
plt.imshow(img_marked)
fig.add_subplot(1, 5, 2)
plt.title('attacked img', fontsize=24)
plt.imshow(img_attacked)
fig.add_subplot(1, 5, 3)
plt.title('marked residual', fontsize=24)
plt.imshow(np.abs(img_marked - img))
fig.add_subplot(1, 5, 4)
plt.title('extracted', fontsize=24)
plt.imshow(reconstruct, cmap='gray')
fig.add_subplot(1, 5, 5)
plt.title('extracted residual', fontsize=24)
plt.imshow(np.abs(reconstruct - watermark), cmap='gray')
plt.show()
series_1_4 = np.array([1, 0, 0, 1, 0]) # for block_size == 4
series_1_8 = np.array([1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1,
0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1]) # for block_size == 8
print(len(qrcode2series(binary_watermark)))
plt.imshow(origin_watermark)
plt.show()
plt.imshow(binary_watermark, cmap='gray')
plt.show()897
def MSE(A, B):
return np.mean(np.power(A-B, 2))
def PSNR(A, B):
return 10 * np.log10(255 / np.sqrt(MSE(A, B)))def no_attack(img):
return img.copy()
def erase_attack(img):
ret = img.copy()
ret[img.shape[0]*3//4:img.shape[0], img.shape[1]*3//4:img.shape[1], :] = 1
return ret
def gaussian_attack(img, intensity):
ret = img.copy()
ret += np.random.normal(0, intensity, ret.shape)
return ret
def salt_pepper(img, n, salt):
ret = img.copy()
val = 1 if salt else 0
for k in range(n):
i = int(np.random.random() * img.shape[0])
j = int(np.random.random() * img.shape[1])
ret[i, j, :] = val
return ret
def jpeg_attack(img, block_size=8):
marked = img.copy()
channel_size = 3
dim1 = (img.shape[0] // block_size)
dim2 = (img.shape[1] // block_size)
for x in range(dim1):
for y in range(dim2):
for channel in range(channel_size):
coef = dct2(img[x*block_size:x*block_size+block_size,
y*block_size:y*block_size+block_size, channel])
for i in range(block_size):
for j in range(max(0, block_size-1-i), block_size):
coef[i, j] = 0
marked[x*block_size:x*block_size+block_size,
y*block_size:y*block_size+block_size, channel] = idct2(coef)
return marked
def hist_attack(img):
ret = (img.copy()[:, :, (2, 1, 0)] * 256).astype(np.uint8)
img_yuv = cv2.cvtColor(ret, cv2.COLOR_BGR2YCrCb)
img_yuv[:, :, 0] = cv2.equalizeHist(img_yuv[:, :, 0])
img_output = cv2.cvtColor(img_yuv, cv2.COLOR_YCrCb2BGR)
return img_output[:, :, (2, 1, 0)] / 256
def color_attack(img, channel):
ret = img.copy()
ret[:, :, channel] = cv2.equalizeHist(
(256 * ret[:, :, channel]).astype(np.uint8)) / 256
return ret
# and some others attack defined in opencv
# cv2.medianBlur(img01,5)
# cv2.blur(img01,(5,5))
# cv2.GaussianBlur(img01,(7,7),0)
# cv2.bilateralFilter(img01,40,75,75)DFT 非盲水印嵌入的想法十分简单，对于图片
在这一过程中，可以通过调整叠加水印图像的权重
def DFT_add_watermark(img, watermark, weight=1):
mask = np.zeros(img.shape)
mask[:watermark.shape[0], :watermark.shape[1], :] = watermark
img_fft = np.fft.fftshift(np.fft.fft2(img))
img_fft += mask * weight
img_marked = np.fft.ifft2(np.fft.ifftshift(img_fft))
return np.abs(img_marked)
def DFT_extract_watermark(img_marked, img):
return np.abs(fft2(img_marked) - fft2(img))
def DFT_test(img, watermark):
img_marked = DFT_add_watermark(img, watermark, 1)
img_extracted = DFT_extract_watermark(img_marked, img)
fig = plt.figure(figsize=(20, 20))
fig.add_subplot(1, 3, 1)
plt.title('marked img', fontsize=36)
plt.imshow(img_marked)
fig.add_subplot(1, 3, 2)
plt.title('residual', fontsize=36)
plt.imshow(np.abs(img_marked - img))
fig.add_subplot(1, 3, 3)
plt.title('extracted', fontsize=36)
plt.imshow(img_extracted)
plt.show()
DFT_test(img, origin_watermark)Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
DWT 的原理这里不再赘述。其会将
同样这里也可以根据嵌入的权重来平衡图像质量和水印稳健性。实验的代码如下，三张结果图分别代表嵌入后的图像，嵌入后的图像对比原图像的残差以及提取出的水印。
def DWT_add_watermark(img, watermark, weight=0.05):
mask = np.zeros((img.shape[0]//2, img.shape[1]//2, img.shape[2]))
mask[:watermark.shape[0], :watermark.shape[1], :] = watermark
(LL, (LH, HL, HH)) = pywt.dwt2(img, 'haar', axes=(0, 1))
coef = (LL, (LH, HL, HH * (1-weight) + mask * weight))
return pywt.idwt2(coef, 'haar', axes=(0, 1))
def DWT_extract_watermark(img_marked, img, weight=0.05):
return (pywt.dwt2(img_marked, 'haar', axes=(0, 1))[1][2] - (1-weight) * pywt.dwt2(img, 'haar', axes=(0, 1))[1][2])
def DWT_test(img, watermark):
img_marked = DWT_add_watermark(img, watermark, 0.05)
img_extracted = DWT_extract_watermark(img_marked, img)
fig = plt.figure(figsize=(20, 20))
fig.add_subplot(1, 3, 1)
plt.title('marked img', fontsize=36)
plt.imshow(img_marked)
fig.add_subplot(1, 3, 2)
plt.title('residual', fontsize=36)
plt.imshow(np.abs(img_marked - img))
fig.add_subplot(1, 3, 3)
plt.title('extracted', fontsize=36)
plt.imshow(np.abs(img_extracted * 100))
plt.show()
DWT_test(img, origin_watermark)Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
DCT 盲水印的核心**，均是先对原图像进行分割（如
量化至奇/偶，顾名思义就是根据改写如的信息是 0 或 1 选择将对应的数字往最近的奇数/偶数取整。这里需要注意的有两点
- 事实上，绝大多数系数可能都会比较接近0，因此量化之时通常会乘一个放大系数
$Q$ 。该放大系数自然也就承担了平衡图像质量和水印稳健性的功能。 - 我们通常处理 DCT 系数的中低频部分，因为图像的能量大都集中在低频部分，对低频修改可能会使得图像质量下降较大。而高频成分则相对更不稳定，且许多图像压缩算法如 JPEG 就是对 DCT 系数丢弃高频然后量化后实现了高质量的压缩。
而提取的时候则重复同样的操作，将嵌入位置的值(乘上相同的量化系数后)进行取整，根据奇数多还是偶数多决定 0/1。
实现的代码如下。五张结果图分别代表嵌入后的图像、嵌入后的图像对比原图像的残差、攻击后的图像、提取出的水印以及提取的水印相对原水印的残差。这里由于 DCT 嵌入的时候没有嵌入到右下角，所以对右下角的裁剪完全不敏感。这个十分朴素的例子其实也是有用的——许多网站会自动将可见的水印直接嵌入在左下角或者右下角，至少这样的方式通常不会对 DCT 的盲水印嵌入带来干扰
def my_round(x, toeven):
if toeven:
return np.round(x/2)*2
else:
return np.round((x + 1)/2)*2-1
def dct2(block):
return dct(dct(block.T, norm='ortho').T, norm='ortho')
def idct2(block):
return idct(idct(block.T, norm='ortho').T, norm='ortho')
def DCT_add_watermark(img, series, Q=1000, block_size=8):
marked = img.copy()
channel_size = 3
dim1 = (img.shape[0] // block_size)
dim2 = (img.shape[1] // block_size)
max_capacity = dim1 * dim2
if series.shape[0] > max_capacity:
print("Cannot embbed the message")
for i in range(series.shape[0]):
x = (i // dim2) * block_size
y = (i % dim2) * block_size
for channel in range(channel_size):
coef = dct2(img[x:x+block_size, y:y+block_size, channel])
coef *= Q
for freq in range(block_size // 2, block_size):
for id in range(freq):
coef[id, freq - 1 -
id] = my_round(coef[id, freq - 1 - id], series[i] == 1)
coef /= Q
marked[x:x+block_size, y:y+block_size, channel] = idct2(coef)
return marked
def DCT_extract_watermark(img_marked, series_len, Q=1000, block_size=8):
channel_size = 3
dim1 = (img_marked.shape[0] // block_size)
dim2 = (img_marked.shape[1] // block_size)
series = np.zeros(series_len)
bit_every_block = channel_size * \
(3 * block_size * block_size - 2 * block_size) // 8
for i in range(series_len):
x = (i // dim2) * block_size
y = (i % dim2) * block_size
votes = 0
for channel in range(channel_size):
coef = dct2(img_marked[x:x+block_size, y:y+block_size, channel])
coef *= Q
for freq in range(block_size // 2, block_size):
for id in range(freq):
if np.round(coef[id, freq - 1 - id]) % 2 == 0:
votes += 1
series[i] = votes
return series / bit_every_block
def DCT_test(img, watermark, Q=1000, attack_method=no_attack, show=True, *args, **kwargs):
series = qrcode2series(watermark)
series = np.repeat(series, 3)
img_marked = DCT_add_watermark(img, series, Q)
img_attacked = attack_method(img_marked, *args, **kwargs)
series2 = DCT_extract_watermark(img_attacked, series.shape[0], Q)
series2 = np.round(
np.mean(series2.reshape((series.shape[0]//3, 3)), axis=1))
reconstruct = series2qrcode(series2)
if show:
fig = plt.figure(figsize=(20, 20))
show_imgs(fig, img, img_marked, img_attacked, watermark, reconstruct)
return img_marked, reconstruct
_ = DCT_test(img, binary_watermark, 10, erase_attack, True, *[])Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
采用了一个更加激进的量化方式——（再乘上量化系数后）根据 0/1 直接修改系数为 -1/1，提取的时候则根据符号来决定还原为 0/1。可想而知这样的算法图像质量会较差但稳健性得到提高。
实现的代码如下。五张结果图分别代表嵌入后的图像、嵌入后的图像对比原图像的残差、攻击后的图像、提取出的水印以及提取的水印相对原水印的残差。从第一张图和第三张图可以看到格点。为此，这里接了一个中值滤波去除这样的类似椒盐的噪点。从第二张图和第四张图可以看到图像质量的改善。而盲水印对这样的中值滤波的攻击也有一定的抵抗能力——最后水印只出现了 1bit 的错误，这对于二维码的扫描完全不成问题。
def impose(is_one):
if is_one:
return 1
else:
return -1
def DCT_add_watermark_impose(img, series, Q=1000, block_size=8):
marked = img.copy()
channel_size = 3
dim1 = (img.shape[0] // block_size)
dim2 = (img.shape[1] // block_size)
max_capacity = dim1 * dim2
if series.shape[0] > max_capacity:
print("Cannot embbed the message")
for i in range(series.shape[0]):
x = (i // dim2) * block_size
y = (i % dim2) * block_size
for channel in range(channel_size):
coef = dct2(img[x:x+block_size, y:y+block_size, channel])
coef *= Q
for freq in range(block_size // 2, block_size):
for id in range(freq):
coef[id, freq - 1 - id] = impose(series[i] == 1)
coef /= Q
marked[x:x+block_size, y:y+block_size, channel] = idct2(coef)
return marked
def DCT_extract_watermark_impose(img_marked, series_len, Q=1000, block_size=8):
channel_size = 3
dim1 = (img_marked.shape[0] // block_size)
dim2 = (img_marked.shape[1] // block_size)
series = np.zeros(series_len)
bit_every_block = channel_size * \
(3 * block_size * block_size - 2 * block_size) // 8
for i in range(series_len):
x = (i // dim2) * block_size
y = (i % dim2) * block_size
votes = 0
for channel in range(channel_size):
coef = dct2(img_marked[x:x+block_size, y:y+block_size, channel])
coef *= Q
for freq in range(block_size // 2, block_size):
for id in range(freq):
if coef[id, freq - 1 - id] > 0:
votes += 1
series[i] = votes
return series / bit_every_block
def DCT_test_impose(img, watermark, Q=1000, attack_method=no_attack, show=True, *args, **kwargs):
series = qrcode2series(watermark)
series = np.repeat(series, 3)
img_marked = DCT_add_watermark_impose(img, series, Q)
img_attacked = attack_method(img_marked, *args, **kwargs)
series2 = DCT_extract_watermark_impose(img_attacked, series.shape[0], Q)
series2 = np.round(
np.mean(series2.reshape((series.shape[0]//3, 3)), axis=1))
reconstruct = series2qrcode(series2)
if show:
fig = plt.figure(figsize=(20, 20))
fig.add_subplot(1, 6, 1)
plt.title('marked img', fontsize=24)
plt.imshow(img_marked)
fig.add_subplot(1, 6, 2)
plt.title('attacked img', fontsize=24)
plt.imshow(img_attacked)
fig.add_subplot(1, 6, 3)
plt.title('marked residual', fontsize=24)
plt.imshow(np.abs(img_marked - img))
fig.add_subplot(1, 6, 4)
plt.title('attacked residual', fontsize=24)
plt.imshow(np.abs(img_attacked - img))
fig.add_subplot(1, 6, 5)
plt.title('extracted', fontsize=24)
plt.imshow(reconstruct, cmap='gray')
fig.add_subplot(1, 6, 6)
plt.title('extracted residual', fontsize=24)
plt.imshow(np.abs(reconstruct - watermark), cmap='gray')
plt.show()
return img_marked, reconstruct
_ = DCT_test_impose(img, binary_watermark, 10, cv2.medianBlur, True, *[3])Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
使用一段预先给出了伪随机序列来作为量化时的目标。当信息为 0/1 时分别量化为该随机序列或其补。提取水印时根据相关系数来得到该信息是 0/1.
实现的代码如下。五张结果图分别代表嵌入后的图像、嵌入后的图像对比原图像的残差、JPEG 压缩攻击后的图像、提取出的水印以及提取的水印相对原水印的残差。可以看到图像质量相比直接量化为 -1/1 有提升。
这里由于选用了 JPEG 压缩攻击，而基于 DCT 对齐天然地不敏感。这个十分朴素的例子同样其实也是有用的——在互联网上使用 JPEG 这样格式的图片是十分常见的。
def DCT_add_watermark_corr(img, series, Q=1000, block_size=8):
series_1 = series_1_4 if block_size == 4 else series_1_8
marked = img.copy()
channel_size = 3
dim1 = (img.shape[0] // block_size)
dim2 = (img.shape[1] // block_size)
max_capacity = dim1 * dim2
if series.shape[0] > max_capacity:
print("Cannot embbed the message")
for i in range(series.shape[0]):
x = (i // dim2) * block_size
y = (i % dim2) * block_size
for channel in range(channel_size):
coef = dct2(img[x:x+block_size, y:y+block_size, channel])
coef *= Q
cnt = 0
for freq in range(block_size // 2, block_size):
for id in range(freq):
coef[id, freq - 1 -
id] = series_1[cnt] if series[i] == 1 else 1-series_1[cnt]
cnt += 1
coef /= Q
marked[x:x+block_size, y:y+block_size, channel] = idct2(coef)
return marked
def DCT_extract_watermark_corr(img_marked, series_len, Q=1000, block_size=8):
series_1 = series_1_4 if block_size == 4 else series_1_8
channel_size = 3
dim1 = (img_marked.shape[0] // block_size)
dim2 = (img_marked.shape[1] // block_size)
series = np.zeros(series_len)
bit_every_block = (3 * block_size * block_size - 2 * block_size) // 8
for i in range(series_len):
x = (i // dim2) * block_size
y = (i % dim2) * block_size
corrs = 0.0
for channel in range(channel_size):
coef = dct2(img_marked[x:x+block_size, y:y+block_size, channel])
coef *= Q
tmp = np.zeros(bit_every_block)
cnt = 0
for freq in range(block_size // 2, block_size):
for id in range(freq):
tmp[cnt] = coef[id, freq - 1 - id]
cnt += 1
corrs += np.corrcoef(series_1, tmp)[0, 1]
series[i] = 1 if corrs > 0 else 0
return series
def DCT_test_corr(img, watermark, Q=1000, attack_method=no_attack, show=True, *args, **kwargs):
series = qrcode2series(watermark)
series = np.repeat(series, 3)
img_marked = DCT_add_watermark_corr(img, series, Q)
img_attacked = attack_method(img_marked, *args, **kwargs)
series2 = DCT_extract_watermark_corr(img_attacked, series.shape[0], Q)
series2 = np.round(
np.mean(series2.reshape((series.shape[0]//3, 3)), axis=1))
reconstruct = series2qrcode(series2)
if show:
fig = plt.figure(figsize=(20, 20))
show_imgs(fig, img, img_marked, img_attacked, watermark, reconstruct)
return img_marked, reconstruct
_ = DCT_test_corr(img, binary_watermark, 25, jpeg_attack, True, *[])Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
比起直接对原图像进行分块然后 DCT，该方法先对图像做一次 DWT，然后取
这里五张图的意义和之前一样就不再赘述了。
def DWT_DCT_add_watermark(img, series, Q=1000, block_size=8):
(LL, (LH, HL, HH)) = pywt.dwt2(img, 'haar', axes=(0, 1))
(LL2, (LH2, HL2, HH2)) = pywt.dwt2(HL, 'haar', axes=(0, 1))
HL2_ = DCT_add_watermark(HL2, series, Q, block_size)
HL_ = pywt.idwt2((LL2, (LH2, HL2_, HH2)), 'haar', axes=(0, 1))
img_ = pywt.idwt2((LL, (LH, HL_, HH)), 'haar', axes=(0, 1))
return img_
def DWT_DCT_extract_watermark(img_marked, series_len, Q=1000, block_size=8):
(LL, (LH, HL, HH)) = pywt.dwt2(img_marked, 'haar', axes=(0, 1))
(LL2, (LH2, HL2, HH2)) = pywt.dwt2(HL, 'haar', axes=(0, 1))
series = DCT_extract_watermark(HL2, series_len, Q, block_size)
return series
def DWT_DCT_test(img, watermark, Q=1000, attack_method=no_attack, show=True, *args, **kwargs):
series = qrcode2series(watermark)
img_marked = DWT_DCT_add_watermark(img, series, Q, 4)
img_attacked = attack_method(img_marked, *args, **kwargs)
series2 = DWT_DCT_extract_watermark(img_attacked, series.shape[0], Q, 4)
reconstruct = series2qrcode(series2)
if show:
fig = plt.figure(figsize=(20, 20))
show_imgs(fig, img, img_marked, img_attacked, watermark, reconstruct)
return img_marked, reconstruct
_ = DWT_DCT_test(img, binary_watermark)Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
其实该方法也可以作为一种量化方式——相比直接对系数矩阵进行修改，他对该系数矩阵先做 SVD 分解，然后选择第一个奇异值进行量化修改，最后再还原回系数矩阵，再通过 IDCT 得到嵌入后的图像。提取的方式也就是上述过程的逆过程。
这里五张图的意义和之前一样就不再赘述了。
def DCT_SVD_add_watermark(img, series, Q=1000):
marked = img.copy()
A = np.zeros((128, 128, 3))
series_len = series.shape[0]
for i in range(128):
for j in range(128):
for channel in range(3):
coef = dct2(img[4*i:4*i+4, 4*j:4*j+4, channel])
A[i, j, channel] = coef[0, 0]
for idx in range(series_len):
i = idx // 32
j = idx % 32
for channel in range(3):
u, s, vh = np.linalg.svd(
A[4*i:4*i+4, 4*j:4*j+4, channel], full_matrices=False)
s[0] = my_round(s[0] * Q, series[idx] == 1) / Q
A[4*i:4*i+4, 4*j:4*j+4, channel] = np.dot(u * s, vh)
for i in range(128):
for j in range(128):
for channel in range(3):
coef = dct2(img[4*i:4*i+4, 4*j:4*j+4, channel])
coef[0, 0] = A[i, j, channel]
marked[4*i:4*i+4, 4*j:4*j+4, channel] = idct2(coef)
return marked
def DCT_SVD_extract_watermark(img_marked, series_len, Q=1000):
A = np.zeros((128, 128, 3))
series = np.zeros(series_len)
for i in range(128):
for j in range(128):
for channel in range(3):
coef = dct2(img_marked[4*i:4*i+4, 4*j:4*j+4, channel])
A[i, j, channel] = coef[0, 0]
for idx in range(series_len):
i = idx // 32
j = idx % 32
votes = 0
for channel in range(3):
u, s, vh = np.linalg.svd(
A[4*i:4*i+4, 4*j:4*j+4, channel], full_matrices=False)
if np.round(s[0] * Q) % 2 == 0:
votes += 1
if votes > 1.5:
series[idx] = 1
else:
series[idx] = 0
return series
def DCT_SVD_test(img, watermark, Q=1000, attack_method=no_attack, show=True, *args, **kwargs):
series = qrcode2series(watermark)
img_marked = DCT_SVD_add_watermark(img, series, Q)
img_attacked = attack_method(img_marked, *args, **kwargs)
series2 = DCT_SVD_extract_watermark(img_attacked, series.shape[0], Q)
reconstruct = series2qrcode(series2)
if show:
fig = plt.figure(figsize=(20, 20))
show_imgs(fig, img, img_marked, img_attacked, watermark, reconstruct)
return img_marked, reconstruct
_ = DCT_SVD_test(img, binary_watermark)Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
同样我们也提出并实现了一个空域盲水印的嵌入算法。其**首先借鉴了 DCT 中分块处理的**——将图像划分为
这里五张图的意义和之前一样就不再赘述了。
def find_best_window(img, window_size):
param = (-1, -1, 100000)
for i in range(img.shape[0] - window_size):
for j in range(img.shape[1] - window_size):
tmp = img[i:i+window_size, j:j+window_size, :]
score = np.sum(np.std(tmp, axis=(0, 1)))
if score < param[2]:
param = (i, j, score)
return param
def spatial_add_watermark(img, series, Q=1000):
marked = img.copy()
window_size = 4
block_size = 16
block_every_row = img.shape[0] // block_size
series_len = series.shape[0]
for idx in range(series_len):
i = idx // block_every_row
j = idx % block_every_row
_x = i * block_size
_y = j * block_size
(x, y, _) = find_best_window(
marked[_x:_x+block_size, _y:_y+block_size, :], window_size)
mu = my_round(Q * np.mean(marked[_x+x:_x+x+window_size, _y +
y:_y+y+window_size, :], axis=(0, 1)), series[idx] == 1) / Q
marked[_x+x:_x+x+window_size, _y+y:_y+y+window_size, :] = np.repeat(
mu, window_size * window_size).reshape((window_size, window_size, 3), order='F')
return marked
def spatial_extract_watermark(img_marked, series_len, Q=1000):
series = np.zeros(series_len)
window_size = 4
block_size = 16
block_every_row = img_marked.shape[0] // block_size
for idx in range(series_len):
i = idx // block_every_row
j = idx % block_every_row
_x = i * block_size
_y = j * block_size
(x, y, _) = find_best_window(
img_marked[_x:_x+block_size, _y:_y+block_size, :], window_size)
mu = np.round(
Q * np.mean(img_marked[_x+x:_x+x+window_size, _y+y:_y+y+window_size, :], axis=(0, 1)))
votes = np.sum(mu % 2 == 0)
series[idx] = 1 if votes > 1.5 else 0
return series
def spatial_test(img, watermark, Q=1000, attack_method=no_attack, show=True, *args, **kwargs):
series = qrcode2series(watermark)
img_marked = spatial_add_watermark(img, series, Q)
img_attacked = attack_method(img_marked, *args, **kwargs)
series2 = spatial_extract_watermark(img_attacked, series.shape[0], Q)
reconstruct = series2qrcode(series2)
if show:
fig = plt.figure(figsize=(20, 20))
show_imgs(fig, img, img_marked, img_attacked, watermark, reconstruct)
return img_marked, reconstruct
_ = spatial_test(img, binary_watermark)
在做这次数字水印的过程中，一个挥之不去的念头就是我们能不能同时使用多种嵌入方式，然后博采众长。毕竟各个算法可能在不同的环境下有不同的表现，如果能同时嵌入的话可能会极大提高水印的稳定性。
这里我们选择了 DCT + 空域的组合。并将两种嵌入方式级联起来。由于是盲水印嵌入，可以想象的时如果不加任何攻击，最后使用的嵌入方法一定可以被完美的提取，而前面的方法嵌入的水印会遭到一定程度的破坏。实验发现空域的嵌入对频域的影响较小，因此我们将其放至最后。
最后的实验结果也是十分喜人——在没有加攻击的情况下，两个水印都能被完美地提取出来，也就说明空域的嵌入没有破坏 DCT 的嵌入。而在加入椒盐噪声后，两个方法提取出来的水印都有一定程度的损失，但都是可以被扫描识别的。这些都说明了混合盲水印的嵌入是有效的。
def hybrid_add_watermark(img, series, Q1=1000, Q2=1000):
tmp_1 = DCT_add_watermark_corr(img, series, Q1)
tmp_2 = spatial_add_watermark(tmp_1, series, Q2)
return tmp_2
def hybrid_test(img, watermark, Q1=1000, Q2=1000, attack_method=no_attack, show=True, *args, **kwargs):
series = qrcode2series(watermark)
img_marked = hybrid_add_watermark(img, series, Q1, Q2)
img_attacked = attack_method(img_marked, *args, **kwargs)
series2_1 = spatial_extract_watermark(img_attacked, series.shape[0], Q1)
reconstruct_1 = series2qrcode(series2_1)
series2_2 = DCT_extract_watermark_corr(img_attacked, series.shape[0], Q2)
reconstruct_2 = series2qrcode(series2_2)
if show:
fig = plt.figure(figsize=(20, 20))
fig.add_subplot(1, 7, 1)
plt.title("marked img", fontsize=16)
plt.imshow(img_marked)
fig.add_subplot(1, 7, 2)
plt.title("attacked img", fontsize=16)
plt.imshow(img_attacked)
fig.add_subplot(1, 7, 3)
plt.title("marked residual", fontsize=16)
plt.imshow(np.abs(img_marked - img))
fig.add_subplot(1, 7, 4)
plt.title("extracted 1", fontsize=16)
plt.imshow(reconstruct_1, cmap='gray')
fig.add_subplot(1, 7, 5)
plt.title("extracted 1 residual", fontsize=16)
plt.imshow(np.abs(reconstruct_1 - watermark), cmap='gray')
fig.add_subplot(1, 7, 6)
plt.title("extracted 2", fontsize=16)
plt.imshow(reconstruct_2, cmap='gray')
fig.add_subplot(1, 7, 7)
plt.title("extracted 2 residual", fontsize=16)
plt.imshow(np.abs(reconstruct_2 - watermark), cmap='gray')
plt.show()
_ = hybrid_test(img, binary_watermark, 10, 10)
_ = hybrid_test(img, binary_watermark, 10, 10, salt_pepper, True, *[1000, True])Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
下面测试了在十种攻击下各个算法的稳健性。关注的指标主要是 MSE，PSNR 以及误码个数。可以得到如下的结论
- DCT_impose 和 DCT_corr 相对来说图像质量会降一些，但水印的稳健性大大提升，可以满足大部分需求
- DWT_DCT 稳健性相对较差，更适合于图像基本不失真的情形
- DCT_SVD 图像的质量显著好于其他的 DCT 方法，但稳健性不如 DCT_impose 和 DCT_corr
- 双边滤波相对来说还是比较难抵抗的
def test_single(img, watermark, encode_method, Q, attack_method, msg, *args, **kwargs):
print_msg = []
img_marked, reconstruct = encode_method(img, watermark, Q, attack_method, False, *args, **kwargs)
print_msg.append(msg)
print_msg.append(str(Q))
print_msg.append(str(MSE(img, img_marked)))
print_msg.append(str(PSNR(img, img_marked)))
print_msg.append(str(np.sum(watermark != reconstruct)))
print(','.join(print_msg))
def test(img, watermark, Q):
attacks = [no_attack, salt_pepper, gaussian_attack, jpeg_attack, erase_attack,
cv2.medianBlur, cv2.blur, cv2.GaussianBlur, cv2.bilateralFilter, hist_attack, color_attack]
names = ["origin", "salt_pepper", "gaussian", "jpeg_attack", "erase_attack",
"medianBlur", "blur", "GaussianBlur", "bilateralFilter", "hist_attack", "color_attack"]
params = [[], [1000, True], [0.01], [], [], [3],
[(3, 3)], [(3, 3), 0], [40, 75, 75], [], [0]]
for i in range(len(attacks)):
test_single(img, watermark, DCT_test, Q,
attacks[i], "{0} + {1}".format(names[i], "DCT_test "), *params[i])
test_single(img, watermark, DCT_test_corr, Q,
attacks[i], "{0} + {1}".format(names[i], "DCT_test_corr "), *params[i])
test_single(img, watermark, DCT_test_impose, Q,
attacks[i], "{0} + {1}".format(names[i], "DCT_test_impose"), *params[i])
test_single(img, watermark, DWT_DCT_test, Q,
attacks[i], "{0} + {1}".format(names[i], "DWT_DCT_test "), *params[i])
test_single(img, watermark, DCT_SVD_test, Q,
attacks[i], "{0} + {1}".format(names[i], "DCT_SVD_test "), *params[i])
test_single(img, watermark, spatial_test, Q,
attacks[i], "{0} + {1}".format(names[i], "spatial_test "), *params[i])
_ = test(img, binary_watermark, 25)origin + DCT_test ,25,0.00012605156,43.562660627883254,0
origin + DCT_test_corr ,25,0.00087038096,39.36685476534186,0
origin + DCT_test_impose,25,0.0010571921,38.94463231582133,0
origin + DWT_DCT_test ,25,9.072689e-06,49.276721704620115,0
origin + DCT_SVD_test ,25,1.8569381e-06,52.72141477912349,0
origin + spatial_test ,25,4.164728e-05,45.96746862923288,0
salt_pepper + DCT_test ,25,0.00012605156,43.562660627883254,7
salt_pepper + DCT_test_corr ,25,0.00087038096,39.36685476534186,0
salt_pepper + DCT_test_impose,25,0.0010571921,38.94463231582133,0
salt_pepper + DWT_DCT_test ,25,9.072689e-06,49.276721704620115,557
salt_pepper + DCT_SVD_test ,25,1.8569381e-06,52.72141477912349,408
salt_pepper + spatial_test ,25,4.164728e-05,45.96746862923288,24
gaussian + DCT_test ,25,0.00012605156,43.562660627883254,0
gaussian + DCT_test_corr ,25,0.00087038096,39.36685476534186,0
gaussian + DCT_test_impose,25,0.0010571921,38.94463231582133,0
gaussian + DWT_DCT_test ,25,9.072689e-06,49.276721704620115,433
gaussian + DCT_SVD_test ,25,1.8569381e-06,52.72141477912349,2
gaussian + spatial_test ,25,4.164728e-05,45.96746862923288,34
jpeg_attack + DCT_test ,25,0.00012605156,43.562660627883254,0
jpeg_attack + DCT_test_corr ,25,0.00087038096,39.36685476534186,0
jpeg_attack + DCT_test_impose,25,0.0010571921,38.94463231582133,0
jpeg_attack + DWT_DCT_test ,25,9.072689e-06,49.276721704620115,193
jpeg_attack + DCT_SVD_test ,25,1.8569381e-06,52.72141477912349,0
jpeg_attack + spatial_test ,25,4.164728e-05,45.96746862923288,89
erase_attack + DCT_test ,25,0.00012605156,43.562660627883254,0
erase_attack + DCT_test_corr ,25,0.00087038096,39.36685476534186,0
erase_attack + DCT_test_impose,25,0.0010571921,38.94463231582133,0
erase_attack + DWT_DCT_test ,25,9.072689e-06,49.276721704620115,16
erase_attack + DCT_SVD_test ,25,1.8569381e-06,52.72141477912349,16
erase_attack + spatial_test ,25,4.164728e-05,45.96746862923288,16
medianBlur + DCT_test ,25,0.00012605156,43.562660627883254,444
medianBlur + DCT_test_corr ,25,0.00087038096,39.36685476534186,7
medianBlur + DCT_test_impose,25,0.0010571921,38.94463231582133,14
medianBlur + DWT_DCT_test ,25,9.072689e-06,49.276721704620115,731
medianBlur + DCT_SVD_test ,25,1.8569381e-06,52.72141477912349,163
medianBlur + spatial_test ,25,4.164728e-05,45.96746862923288,251
blur + DCT_test ,25,0.00012605156,43.562660627883254,451
blur + DCT_test_corr ,25,0.00087038096,39.36685476534186,19
blur + DCT_test_impose,25,0.0010571921,38.94463231582133,12
blur + DWT_DCT_test ,25,9.072689e-06,49.276721704620115,706
blur + DCT_SVD_test ,25,1.8569381e-06,52.72141477912349,116
blur + spatial_test ,25,4.164728e-05,45.96746862923288,324
GaussianBlur + DCT_test ,25,0.00012605156,43.562660627883254,449
GaussianBlur + DCT_test_corr ,25,0.00087038096,39.36685476534186,1
GaussianBlur + DCT_test_impose,25,0.0010571921,38.94463231582133,0
GaussianBlur + DWT_DCT_test ,25,9.072689e-06,49.276721704620115,720
GaussianBlur + DCT_SVD_test ,25,1.8569381e-06,52.72141477912349,73
GaussianBlur + spatial_test ,25,4.164728e-05,45.96746862923288,228
bilateralFilter + DCT_test ,25,0.00012605156,43.562660627883254,451
bilateralFilter + DCT_test_corr ,25,0.00087038096,39.36685476534186,435
bilateralFilter + DCT_test_impose,25,0.0010571921,38.94463231582133,443
bilateralFilter + DWT_DCT_test ,25,9.072689e-06,49.276721704620115,451
bilateralFilter + DCT_SVD_test ,25,1.8569381e-06,52.72141477912349,448
bilateralFilter + spatial_test ,25,4.164728e-05,45.96746862923288,479
hist_attack + DCT_test ,25,0.00012605156,43.562660627883254,310
hist_attack + DCT_test_corr ,25,0.00087038096,39.36685476534186,36
hist_attack + DCT_test_impose,25,0.0010571921,38.94463231582133,24
hist_attack + DWT_DCT_test ,25,9.072689e-06,49.276721704620115,740
hist_attack + DCT_SVD_test ,25,1.8569381e-06,52.72141477912349,447
hist_attack + spatial_test ,25,4.164728e-05,45.96746862923288,520
color_attack + DCT_test ,25,0.00012605156,43.562660627883254,0
color_attack + DCT_test_corr ,25,0.00087038096,39.36685476534186,0
color_attack + DCT_test_impose,25,0.0010571921,38.94463231582133,0
color_attack + DWT_DCT_test ,25,9.072689e-06,49.276721704620115,594
color_attack + DCT_SVD_test ,25,1.8569381e-06,52.72141477912349,0
color_attack + spatial_test ,25,4.164728e-05,45.96746862923288,0
def demo():
_ = DCT_test_impose(img, binary_watermark, 15, salt_pepper, True, *[10000, True])
_ = DCT_test_impose(img, binary_watermark, 20, gaussian_attack, True, *[0.3])
_ = DCT_test_corr(img, binary_watermark, 25, jpeg_attack, True, *[])
_ = DCT_test_corr(img, binary_watermark, 25, erase_attack, True, *[])
_ = DCT_test_corr(img, binary_watermark, 25, cv2.medianBlur, True, *[3])
_ = DCT_test_corr(img, binary_watermark, 25, cv2.blur, True, *[(3, 3)])
_ = DCT_test_corr(img, binary_watermark, 25, cv2.GaussianBlur, True, *[(3, 3), 0])
_ = DCT_test_impose(img, binary_watermark, 25, cv2.GaussianBlur, True, *[(3, 3), 0])
_ = DCT_test_corr(img, binary_watermark, 20, hist_attack, True, *[])
_ = DCT_test_corr(img, binary_watermark, 20, color_attack, True, *[1])
demo()Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
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