基于DWT的数字图像信息隐藏算法代码，带有高斯噪声攻击，可以运行，载体图像和水印图像经过灰度处理，有提取和嵌入水印算法，用python实现

由于您没有提供具体的数字图像信息隐藏算法，以下是一个基于DWT的数字图像水印嵌入和提取的Python实现示例，其中包括高斯噪声攻击：

import numpy as np
import cv2
from pywt import dwt2, idwt2

def embed_watermark(img_path, watermark_path, alpha):
    # 读取载体图像和水印图像
    img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
    watermark = cv2.imread(watermark_path, cv2.IMREAD_GRAYSCALE)
    
    # 对图像进行DWT变换
    coeffs = dwt2(img, 'haar')
    cA, (cH, cV, cD) = coeffs

    # 对水印图像进行DWT变换
    w_coeffs = dwt2(watermark, 'haar')
    w_cA, (w_cH, w_cV, w_cD) = w_coeffs
    
    # 水印嵌入
    new_cA = cA + alpha * w_cA
    new_cH = cH + alpha * w_cH
    new_cV = cV + alpha * w_cV
    new_cD = cD + alpha * w_cD
    
    # 对嵌入后的图像进行IDWT变换
    new_coeffs = (new_cA, (new_cH, new_cV, new_cD))
    watermarked_img = idwt2(new_coeffs, 'haar')
    
    # 添加高斯噪声攻击
    mean = 0
    var = 0.1
    sigma = var ** 0.5
    gauss = np.random.normal(mean, sigma, img.shape)
    noisy_img = img + gauss
    
    # 返回嵌入后的图像和添加高斯噪声攻击后的图像
    return watermarked_img, noisy_img

def extract_watermark(img_path, alpha, watermarked_img_path):
    # 读取嵌有水印的图像
    watermarked_img = cv2.imread(watermarked_img_path, cv2.IMREAD_GRAYSCALE)
    
    # 对嵌有水印的图像进行DWT变换
    coeffs = dwt2(watermarked_img, 'haar')
    cA, (cH, cV, cD) = coeffs
    
    # 提取水印
    extracted_cA = (cA - cA.mean()) / alpha
    extracted_cH = (cH - cH.mean()) / alpha
    extracted_cV = (cV - cV.mean()) / alpha
    extracted_cD = (cD - cD.mean()) / alpha
    
    # 对提取的图像进行IDWT变换
    extracted_coeffs = (extracted_cA, (extracted_cH, extracted_cV, extracted_cD))
    extracted_watermark = idwt2(extracted_coeffs, 'haar')
    
    # 返回提取的水印图像
    return extracted_watermark

需要注意的是，该示例中使用的是haar小波变换，您可以根据需要更改为其他小波变换。此外，高斯噪声的参数也可以根据需要进行调整。