文档图像隐写的深度学习代码

文档图像隐写是一种将秘密信息隐藏在看似普通的文本或图片中的技术。深度学习可以用于文档图像隐写，特别是在图像隐写方面。以下是一些深度学习的代码示例：

1. 使用卷积神经网络实现图像隐写

import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Input
from tensorflow.keras.models import Model

# 定义卷积神经网络
def create_model(input_shape):
    input_layer = Input(shape=input_shape)
    x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_layer)
    x = MaxPooling2D((2, 2), padding='same')(x)
    x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
    x = MaxPooling2D((2, 2), padding='same')(x)
    x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)
    encoded = MaxPooling2D((2, 2), padding='same')(x)

    x = Conv2D(128, (3, 3), activation='relu', padding='same')(encoded)
    x = UpSampling2D((2, 2))(x)
    x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
    x = UpSampling2D((2, 2))(x)
    x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
    x = UpSampling2D((2, 2))(x)
    decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)

    autoencoder = Model(input_layer, decoded)
    autoencoder.compile(optimizer='adam', loss='mse')

    return autoencoder

# 加载数据
def load_data():
    img = cv2.imread('image.png')
    img = cv2.resize(img, (256, 256))

    return np.array(img)

# 将数据随机隐藏
def hide_data(img):
    for i in range(256):
        for j in range(256):
            if np.random.random() > 0.8:
                img[i][j] = [0, 0, 0]

    return img

# 训练模型并保存
def train_model():
    img = load_data()
    hidden_img = hide_data(img)

    model = create_model(input_shape=(256, 256, 3))
    model.fit(hidden_img, img, epochs=50, batch_size=32)

    model.save('model.h5')

# 加载模型并解密数据
def decrypt_data():
    img = load_data()
    hidden_img = hide_data(img)

    model = tf.keras.models.load_model('model.h5')
    decoded_img = model.predict(np.array([hidden_img]))

    cv2.imshow('Original Image', img)
    cv2.imshow('Hidden Image', hidden_img)
    cv2.imshow('Decoded Image', decoded_img[0])
    cv2.waitKey(0)

if __name__ == '__main__':
    train_model()
    decrypt_data()

2. 使用循环神经网络实现文本隐写

import numpy as np
import tensorflow as tf

# 加载数据
def load_data():
    with open('text.txt', 'r') as f:
        text = f.read()

    return text

# 将数据随机加密
def encrypt_data(text):
    encrypted_text = []
    for char in text:
        if np.random.random() > 0.8:
            encrypted_text.append(ord(char))
        else:
            encrypted_text.append(ord(' '))

    return np.array(encrypted_text)

# 定义循环神经网络
def create_model(input_shape):
    input_layer = tf.keras.layers.Input(shape=input_shape)
    x = tf.keras.layers.Embedding(input_dim=128, output_dim=64)(input_layer)
    x = tf.keras.layers.LSTM(64)(x)
    output_layer = tf.keras.layers.Dense(1, activation='sigmoid')(x)

    model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

    return model

# 训练模型并保存
def train_model():
    text = load_data()
    encrypted_text = encrypt_data(text)

    model = create_model(input_shape=(len(text),))
    model.fit(encrypted_text, np.ones(len(text)), epochs=50, batch_size=32)

    model.save('model.h5')

# 加载模型并解密数据
def decrypt_data():
    text = load_data()
    encrypted_text = encrypt_data(text)

    model = tf.keras.models.load_model('model.h5')
    predicted_text = model.predict(encrypted_text)

    decrypted_text = ''
    for i, char in enumerate(text):
        if encrypted_text[i] > 0:
            decrypted_text += chr(int(round(predicted_text[i][0])))
        else:
            decrypted_text += char

    print(decrypted_text)

if __name__ == '__main__':
    train_model()
    decrypt_data()