隐写

效果

隐写前

隐写后

灰度值直方图差别

部分源码

def dec_to_bin(dec):

return '{:08b}'.format(dec)

def bin_to_dec(binary_code):

dec = 0

for i in range(len(binary_code) - 1):

dec = dec + int(binary_code[i]) * int(pow(2, 7 - i))

return dec

# 文件信息转二进制流

def read_data_file(path):

fp = open(path, "rb")

stream = ""

s = fp.read()

for i in range(len(s)):

tmp = bin(s[i]).zfill(8)

stream = stream + tmp.replace('0b', '')

fp.close()

return stream

def lsb(image, data_stream, random_index):

for i in range(len(stream)):

x = random_index[i] % image.shape[0]

y = int(random_index[i] / image.shape[0])

value = image[x, y]

if value % 2 != stream[i]:

if value % 2 == 1:

image[x, y] = value - 1

else:

image[x, y] = value + 1

return image

分析

效果

由于二次隐写的随机性，分析图片存在误差，但能够看出是否被隐写

原图

隐写后

部分源码

# 进行二次隐写

def random_steg(image, rate):

pixel_len = image.shape[0] * image.shape[1]

random_ls = random.sample(range(0, pixel_len), int(pixel_len * rate))

random_ls.sort()

for i in random_ls:

k = random.randint(0, 1)

x = i % image.shape[0]

y = int(i / image.shape[0])

value = image[x, y]

if not value % 2 == k:

if value % 2 == 1:

image[x, y] = value - 1

else:

image[x, y] = value + 1

return image

# 获取灰度值

def get_gary_value(my_img):

pixel_value = []

gary_index = []

for i in range(256):

pixel_value.append(0)

gary_index.append(i)

for i in range(my_img.shape[0]):

for j in range(my_img.shape[1]):

pixel_value[my_img[i][j]] = pixel_value[my_img[i][j]] + 1

return pixel_value, gary_index

# 计算F1,F2

def calculate_f1f2(values):

f1 = 0

f2 = 0

for i in range(128):

tmp = abs(values[2 * i + 1] - values[2 * i])

f1 += tmp

for j in range(127):

tmp = abs(values[2 * j + 2] - values[2 * j + 1])

f2 += tmp

f2 += abs(values[0] - values[255])

return f1, f2

# 分析函数

def analysis(path):

img = cv2.imread(path, 0)

# 二次随机隐写

F1 = []

F2 = []

index = []

for k in range(11):

rate = k / 10

index.append(rate)

new_img = random_steg(img, rate)

new_count, new_index = get_gary_value(new_img)

f_1, f_2 = calculate_f1f2(new_count)

F1.append(f_1)

F2.append(f_2)

draw(F1, F2, index)

相关链接

项目链接

参考文档