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本文展示论文《基于自适应哈夫曼编码的密文可逆信息隐藏算法》的复现代码。代码块的结构如下#xff1a; 其中#xff0c;每个代码块都包含了测试该代码块的功能的主函数代码#xff0c;使用时可放心运行#xff0c;前提是你按照这个包结构把文件命名改好#xff0c…前言
本文展示论文《基于自适应哈夫曼编码的密文可逆信息隐藏算法》的复现代码。代码块的结构如下 其中每个代码块都包含了测试该代码块的功能的主函数代码使用时可放心运行前提是你按照这个包结构把文件命名改好放在同一个文件夹下放好。.idea和__pycache__文件不用管。还有main.py、InformationEmbedding.py也是多余的文件。 论文内容解析请参考本人的这篇文章
论文简述基于自适应哈夫曼编码的密文可逆信息隐藏算法基于位图压缩的加密图像可逆信息隐藏算法_zrc007007的博客-CSDN博客 精简版代码在这里
论文复现代码《基于自适应哈夫曼编码的密文可逆信息隐藏算法》导演剪辑版-CSDN博客 复现代码
MedianEdgeDetector.py
首先是中值预测器文件名为MedianEdgeDetector.py
import numpy as npclass MED:def __init__(self):# 示例数据self.data [[162, 162, 162, 161, 162, 157, 163, 161],[162, 162, 162, 161, 162, 157, 163, 161],[162, 162, 162, 161, 162, 157, 163, 161],[162, 162, 162, 161, 162, 157, 163, 161],[162, 162, 162, 161, 162, 157, 163, 161],[164, 164, 158, 155, 161, 159, 159, 160],[160, 160, 163, 158, 160, 162, 159, 156],[159, 159, 155, 157, 158, 159, 156, 157]]def detect(self, data):# 中值预测result np.zeros_like(data)result[0][0] data[0][0]for i in range(1, len(data)):result[i][0] data[i][0]result[0][i] data[0][i]for i in range(1, len(data)):for j in range(1, len(data[0])):a data[i - 1][j - 1]b data[i - 1][j]c data[i][j - 1]if a min(b, c):result[i][j] max(b, c)elif a max(b, c):result[i][j] min(b, c)else:result[i][j] b c - areturn resultif __name__ __main__:print(MED().detect(MED().data))AdaptiveHuffmanEncoding.py
对图像进行自适应哈夫曼编码文件名为AdaptiveHuffmanEncoding.py
from MedianEdgeDetector import MED
import numpy as np
from math import infclass HuffCode:def compare(self, num1, num2):# 比较像素值的相同比特位数count 0for i in range(8):divisor pow(2, 7-i)if int(num1 // divisor) int(num2 // divisor):count 1num1 num1 - divisornum2 num2 - divisorelse:breakreturn countdef count(self, origindata, data):# 生成图像相同像素值的比特位数的比较结果result {}resultMap np.zeros_like(data)resultMap[0][0] -1for i in range(1, len(data)):resultMap[i][0] -1resultMap[0][i] -1for i in range(1, len(data)):for j in range(1, len(data[0])):num self.compare(origindata[i][j], data[i][j])resultMap[i][j] numresult[num] result.get(num, 0) 1return resultMap, result# 初始化哈夫曼结点
class Huffmannode(object):def __init__(self):self.parent0self.left0self.right0self.weight0# 选择最小的结点下标
def select_node(huffman):# 俩个结点直接返回不需要找最小俩个结点if len(huffman)2:return 0,1minsemininf# 初始化成无穷大fs-1for i in range(len(huffman)):if huffman[i].parent0:if minhuffman[i].weight:seminminsfminhuffman[i].weightfielif seminhuffman[i].weight:seminhuffman[i].weightsireturn f,s# 编码
def Huffman_code(origin_dict):# 给结点赋权重nlen(origin_dict)m2*n-1huffman[]for i in origin_dict:temp_huffmannodeHuffmannode()temp_huffmannode.weightorigin_dict[i]huffman.append(temp_huffmannode)# 构建Huffman树选择俩个最小的结点合并for i in range(n,m):f,sselect_node(huffman)temp_huffmannodeHuffmannode()temp_huffmannode.weighthuffman[f].weighthuffman[s].weighttemp_huffmannode.rightf # 小的放在右边temp_huffmannode.leftshuffman[f].parenthuffman[s].parentihuffman.append(temp_huffmannode)# 0,1编码右0左1codeing_dict dict.fromkeys(origin_dict, None)for i in range(0,n):skiparenthuffman[i].parentwhile parent!0:if huffman[parent].leftk:s1kparentparenthuffman[parent].parentelse:s0kparentparenthuffman[parent].parentcodeing_dict[list(origin_dict.keys())[i]]list(reversed(s))for k in codeing_dict.items():codeing_dict[k[0]] .join(k[1])return codeing_dictif __name__ __main__:MED MED()origindata MED.dataMEDdata MED.detect(origindata)print(np.array(origindata))print(MEDdata)print(HuffCode().compare(159, 160))resultMap, result HuffCode().count(origindata, MEDdata)print(result)# 输入原始字符集s input(输入即将被编码的字符)# 创建字典计算频率dic {}for i in range(len(s)):# get方法如果有键返回该键对应的值如果没键可以设置返回值dic[s[i]] dic.get(s[i], 0) 1code_dict Huffman_code(result)print(code_dict)Crypting.py
混沌矩阵加密文件名为Crypting.py
import numpy as np
from skimage import io
import matplotlib.pyplot as pltdef crypt(im, miu, x0):logistic混沌加密:param im: 图像:param miu: μ参数:param x0: x0参数:return: 返回密文图像h, w im.shapec_ np.zeros_like(im)c_[0][0] x0c[]c.append(x0)result np.zeros_like(im)result[0][0] (x0 * pow(2, 48)) % 256for x in range(1, h * w):i x // wj x % wc.append(miu * c[x - 1] * (1 - c[x - 1]))c_[i][j] (c[x] * pow(2, 48)) % 256for i in range(h):for j in range(w):result[i][j] im[i][j] ^ c_[i][j]return resultdef transform(img):column, row img.shaperesult np.zeros_like(img, dtypeint_)for i in range(column):for j in range(row):result[i][j] int(img[i][j] * 255)return resultif __name__ __main__:plt.set_cmap(cmapgray)img io.imread(../img/lena_grey.tif)print(img)plt.imshow(img)img transform(img)print(img)plt.figure()enc_img crypt(img, 3.6, 0.5)plt.imshow(enc_img)plt.figure()dec_img crypt(enc_img, 3.6, 0.5)plt.imshow(dec_img)plt.show() LabelMapEmbedding.py
位图嵌入文件名LabelMapEmbedding.py
import numpy as np
import math
from AdaptiveHuffmanEncoding import *
from Crypting import *
# from MedianEdgeDetector import *def int2bin(num, leng):整数int转二进制格式binTransform a integer to a string whose length is leng.:param num: numbers waiting for transforming:param leng: the length of hoped result string:return: a string of leng lengthif leng 1:raise Exception(Length isnt positive integer!)place 1result while place leng:result str(num % 2) resultnum num // 2if num 0:return (leng - place) * 0 resultplace 1raise Exception(Length of binary string isnt enough!)def bin2int(string, leng):二进制格式bin转整数intTransfer a string of length of leng to a positive integer:param string: string waiting to be transferred:param leng: the length of reading:return: a positive integer less than 2^lengif leng 1:raise Exception(Length isnt positive integer!)if len(string) leng:raise Exception(Length of string under transferring isnt enough!)place 1result 0while place leng:result int(string[place - 1]) * pow(2, leng - place)place 1return resultdef binstrcomplmt(string, length, complement):给二进制格式补位补到前面。比如补0就给complement传0补1就传1Complement strings with which we decide.:param string: string waiting to be complemented:param length: what we hope it to length:param complement: using what character to complement it:return: string already complementedreturn (length - len(string)) * complement stringdef binstrbackcomplmt(string, length, complement):给二进制格式补位补到后面Complement strings with which we decide on its backhand.:param string: string waiting to be complemented:param length: what we hope it to length:param complement: using what character to complement it:return: string already complementedreturn string (length - len(string)) * complementdef generatec(dict):生成嵌入信息中的c1到c8generate c info:param dict: Huffman code diction:return:result c []for i in range(9):c.append(0)c[i] len(dict.get(i, ))result int2bin(c[i], 4)print(c)return resultdef generateh(dict):生成嵌入信息中的h1到h8generate h info:param dict: Huffman code diction:return:result h []for i in range(9):h.append()h[i] binstrcomplmt(dict.get(i, 0), 8, 0)result h[i]print(h)return resultdef countLm(countDict, lenDict):生成论文中说的Lm变成Eta还需要转换一下Counting Lm:param countDict: diction of Huffman codes appearance:param lenDict: Huffman code diction:return:count 0for i in range(9):count countDict.get(i, 0) * len(lenDict.get(i, ))return countdef generatef(Lm, shapeOfImg):生成嵌入信息中的fgenerate f, which is Lms bits nums binary format:param Lm::param shapeOfImg::return:return int2bin(Lm, math.ceil(np.log2(shapeOfImg[0])) math.ceil(np.log2(shapeOfImg[1])) 2)def generateEta(dic, labelmap):生成嵌入信息中的EtaTransferred label maps binary format:param dic::param labelmap::return:result for i in range(1, labelmap.shape[0]):for j in range(1, labelmap.shape[1]):result dic[labelmap[i][j]]return resultdef generatelabelinfo(HuffDict, CountDict, LabelMap):生成总的嵌入信息Generate embedding information of label map:param HuffDict: Huffman code diction:param CountDict: Length of Huffman codes diction:param LabelMap: The label map:return: Embedding information of label mapreturn generatec(HuffDict) generateh(HuffDict) generatef(countLm(CountDict,HuffDict), LabelMap.shape)\ generateEta(HuffDict, LabelMap)def embedinfo(LabelInfo, EncryptedImg, LabelMap, data):# 嵌入位图信息ReferencePixels result np.array(EncryptedImg)for i in range(EncryptedImg.shape[0]):ReferencePixels int2bin(EncryptedImg[0][i], 8)result[0][i] bin2int(LabelInfo, 8)LabelInfo LabelInfo[8:] # 截掉8个for i in range(1, EncryptedImg.shape[1]):ReferencePixels int2bin(EncryptedImg[i][0], 8)result[i][0] bin2int(LabelInfo, 8)LabelInfo LabelInfo[8:]EmbeddingInfo LabelInfo ReferencePixels dataprint(Length of Reference Pixels is:, len(ReferencePixels))count 0row count // (EncryptedImg.shape[1] - 1) 1column count % (EncryptedImg.shape[1] - 1) 1t LabelMap[row][column]maximum (EncryptedImg.shape[0] - 1) * (EncryptedImg.shape[1] - 1)while len(EmbeddingInfo) 0:if 0 t 6:result[row][column] EncryptedImg[row][column] % pow(2, 7 - t)\ bin2int(binstrbackcomplmt(EmbeddingInfo[:t1], 8, 0), 8)EmbeddingInfo EmbeddingInfo[t1:]elif 7 t 8:result[row][column] bin2int(binstrbackcomplmt(EmbeddingInfo[:8], 8, 0), 8)EmbeddingInfo EmbeddingInfo[8:]count 1if count maximum:raise Exception(Theres no room for embedding!)row count // (EncryptedImg.shape[1] - 1) 1column count % (EncryptedImg.shape[1] - 1) 1t LabelMap[row][column]return resultdef detect(data):测试用预测函数:param data::return:result np.zeros_like(data)result[0][0] data[0][0]for i in range(1, len(data)):result[i][0] data[i][0]result[0][i] data[0][i]for i in range(1, len(data)):for j in range(1, len(data[0])):a data[i - 1][j - 1]b data[i - 1][j]c data[i][j - 1]if a min(b, c):result[i][j] max(b, c)elif a max(b, c):result[i][j] min(b, c)else:result[i][j] b c - areturn resultif __name__ __main__:print(128//2)print(128%2)strng int2bin(1, 8)print(strng)print(bin2int(strng,8))print(bin2int(1, 1))MED MED()origindata MED.dataorigindata np.array(origindata)MEDdata MED.detect(origindata)print(The origin data is:)print(np.array(origindata))print(The predicted data is:)print(MEDdata)print(HuffCode().compare(159, 160))resultMap, countingResult HuffCode().count(origindata, MEDdata)print(The label map is:)print(resultMap)print(The counting result of Huffman coding is:, countingResult)# 输入原始字符集s input(输入即将被编码的字符)# 创建字典计算频率dic {}for i in range(len(s)):# get方法如果有键返回该键对应的值如果没键可以设置返回值dic[s[i]] dic.get(s[i], 0) 1code_dict Huffman_code(countingResult)print(code_dict)generatec(code_dict)print(generatec(code_dict))print(len(generatec(code_dict)))print(generateh(code_dict), len(generateh(code_dict)))lm countLm(countingResult, code_dict)print(Lm is:, lm)f generatef(lm, MEDdata.shape)print(f is:, f)print(length of f is:, len(f))Eta generateEta(code_dict, resultMap)print(The length of Eta is:, len(Eta))print(Eta is:)print(Eta)labelInfo generatelabelinfo(code_dict,countingResult, resultMap)print(The length of labelInfo is:, len(labelInfo))print(Label info:)print(labelInfo)print(Is length of c, h, f and Eta equals to labelInfo:, len(generatec(code_dict)) len(generateh(code_dict)) \len(f) len(Eta) len(labelInfo))embedded embedinfo(labelInfo, crypt(origindata, 3.6, 0.5), resultMap, 0110)print(embedded)strng int2bin(903, 11)print(strng)print(strng[12:] )print(strng[:3])print(detect(origindata))DataExtractionandImageRecovery.py
数据提取和恢复文件名DataExtractionandImageRecovery.py
from LabelMapEmbedding import *
from skimage import io
import matplotlib.pyplot as pltdef HuffDecode(HuffDict, info):# 哈夫曼解码for i in range(9):if info[:len(HuffDict.get(i, ))] HuffDict.get(i, -1):print(The i is:, i)print(info[:8])print(HuffDict)return i, len(HuffDict[i])raise Exception(No string matches!)def extractpixel(pixel, t):# 根据t值恢复像素位数if 0 t 7:return int2bin(pixel, 8)[:t1]elif t 8:return int2bin(pixel, 8)else:raise Exception(T out of range!)def calctcut(t):# 根据t值返回像素数if 0 t 7:return t 1elif t 8:return 8else:raise Exception(T out of range!)def extract1(encrypted):# 提取第一行、第一列info LenDict {}HuffDict {}for i in range(encrypted.shape[0]):info int2bin(encrypted[0][i], 8)for i in range(1, encrypted.shape[1]):info int2bin(encrypted[i][0], 8)for i in range(9):LenDict[i] bin2int(info[:4], 4)info info[4:]for i in range(9):if LenDict[i] ! 0:HuffDict[i] info[8-LenDict[i]:8]info info[8:]print(LenDict, HuffDict)return HuffDict, infodef extract_exp(encrypted, HuffDict, info, datalen, Label): # Label是用来测试位图和算出来的是不是一样的可以删掉# 提取位图LabelMap np.zeros_like(encrypted)decrypted1 np.array(encrypted)LmLen math.ceil(np.log2(encrypted.shape[0])) math.ceil(np.log2(encrypted.shape[1])) 2Lm bin2int(info[:LmLen], LmLen)info info[LmLen:]infoLen Lm ((encrypted.shape[0] - 1) (encrypted.shape[1] - 1) - 1) * 8 datalencount len(info) # 已提取到的信息长度place 0pursuit 0gonext 0count1 1while count infoLen:print(count:, count)print(Info len:, infoLen)print(pursuit:, pursuit)row place // (encrypted.shape[1] - 1) 1column place % (encrypted.shape[1] - 1) 1print(origin row, column:, row, column)if count1 1 and count Lm:t, CodeLen HuffDecode(HuffDict, info)print(t is:, t)LabelMap[row][column] tinfo info[CodeLen:] extractpixel(encrypted[row][column], t)count calctcut(t)place 1print(step1)print(Is the Label Map equal to the extrated one:, Label[row][column] t)print(place is:, place)print(count is:, count)else:pursuit 1count1 0if pursuit 1 and gonext 0:record placeprint(\nThe staring of step 2s row and column is:\n, row , column)while place (encrypted.shape[0] - 1) * (encrypted.shape[1] - 1):row place // (encrypted.shape[1] - 1) 1column place % (encrypted.shape[1] - 1) 1t, CodeLen HuffDecode(HuffDict, info)LabelMap[row][column] tinfo info[CodeLen:] extractpixel(encrypted[row][column], t)print(step2)print(Is the Label Map equal to the extrated one:, Label[row][column] t)print(T is {}, and the Label is {}.format(t, Label[row][column]))print(place:, place)print(row, column:, row, column)place 1print(haha,place)print(end)place recordrow place // (encrypted.shape[1] - 1) 1column place % (encrypted.shape[1] - 1) 1gonext 1if gonext 1 and count - Lm (encrypted.shape[0] - 1) * (encrypted.shape[1] - 1) * 8 datalen:# print(place:,place)# print(row, column:, row, column)info extractpixel(encrypted[row][column], LabelMap[row][column])count calctcut(LabelMap[row][column])# print(infolen:, infoLen)# print(count:, count)# print()place 1for i in range(encrypted.shape[0]):LabelMap[0][i] -1decrypted1[0][i] bin2int(info[:8], 8)info info[8:]for i in range(1, encrypted.shape[1]):LabelMap[i][0] -1decrypted1[i][0] bin2int(info[:8], 8)info info[8:]data info[:datalen]return LabelMap, decrypted1, datadef reverse(char):# 反转0和1if char 0:return 1elif char 1:return 0else:raise Exception(Not 0 or 1!)def detect(data, i, j):测试用预测函数:param data::return:a data[i - 1][j - 1]b data[i - 1][j]c data[i][j - 1]if a min(b, c):result max(b, c)elif a max(b, c):result min(b, c)else:result b c - areturn resultdef recovery(img, LabelMap):# 恢复图像result np.zeros_like(img)for i in range(img.shape[0]):result[0][i] img[0][i]for i in range(1, img.shape[1]):result[i][0] img[i][0]for i in range(1, img.shape[0]):for j in range(1, img.shape[1]):px detect(result, i, j)t LabelMap[i][j]if t 8:result[i][j] pxelif 0 t 7:result[i][j] bin2int(int2bin(px, 8)[:t] 0 * (8 - t), 8)\ int(reverse(int2bin(px, 8)[t:t1])) * pow(2, 7 - t) img[i][j] % pow(2, 7 - t)else:raise Exception(T out of range!)return resultif __name__ __main__:MED MED()# origindata MED.dataorigindata io.imread(../img/lena_grey.tif)origindata transform(origindata)origindata np.array(origindata)MEDdata MED.detect(origindata)print(The origin data is:)print(np.array(origindata))print(The predicted data is:)print(MEDdata)print(HuffCode().compare(159, 160))resultMap, countingResult HuffCode().count(origindata, MEDdata)print(The label map is:)print(resultMap)print(The counting result of Huffman coding is:, countingResult)code_dict Huffman_code(countingResult)print(code_dict)generatec(code_dict)print(generatec(code_dict))print(len(generatec(code_dict)))print(generateh(code_dict), len(generateh(code_dict)))lm countLm(countingResult, code_dict)print(Lm is:, lm)f generatef(lm, MEDdata.shape)print(f is:, f)print(length of f is:, len(f))Eta generateEta(code_dict, resultMap)print(The length of Eta is:, len(Eta))print(Eta is:)print(Eta)labelInfo generatelabelinfo(code_dict,countingResult, resultMap)print(The length of labelInfo is:, len(labelInfo))print(Label info:)print(labelInfo)secret 0110010000 * 50 # 用于测试的秘密信息embedded embedinfo(labelInfo, crypt(origindata, 3.6, 0.5), resultMap, secret) # 嵌入秘密信息print(embedded)huff_code, info1 extract1(embedded)print(The length of rest of info waiting for extracting is:, len(info1))print(int2bin(100, 8))print(extractpixel(100, 0))# resultMap2, decrypted1, data extract2(embedded, huff_code, info1, 4)resultMap2, decrypted1, data extract_exp(embedded, huff_code, info1, len(secret), resultMap)print(The encrypted img is:\n, crypt(origindata, 3.6, 0.5))print(The decrypted1 img is:\n, decrypted1)print(resultMap2)print(resultMap)print(data)if secret data:print(THE extract is successful!)img1 crypt(decrypted1, 3.6, 0.5)print(img1 is:\n,img1)print(Origin:)print(origindata)print(Detect img:)print(detect(img1))print(Detected origin data:)print(detect(origindata))res img1 - origindataprint(result compare with origin data:\n)print(res)img2 recovery(img1, resultMap2)print(img2)res img2 - origindataprint(img2 compares to origin:\n, res)plt.set_cmap(cmapgray)plt.subplot(221)plt.imshow(origindata)plt.title(Origin imagery)plt.subplot(222)plt.imshow(embedded)plt.title(Embedded imagery)plt.subplot(223)plt.imshow(img1)plt.title(Preliminary decrypted imagine)plt.subplot(224)plt.imshow(img2)plt.title(Fully recovered imagine)plt.show()Comparison.py
计算指标比较结果文件名Comparison.py
import numpy as np
import math
import cv2
from DataExtractionandImageRecovery import *
from skimage.color import rgb2graydef ssim(img1, img2):#计算ssim指标C1 (0.01 * 255)**2C2 (0.03 * 255)**2img1 img1.astype(np.float64)img2 img2.astype(np.float64)kernel cv2.getGaussianKernel(11, 1.5)window np.outer(kernel, kernel.transpose())mu1 cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # validmu2 cv2.filter2D(img2, -1, window)[5:-5, 5:-5]mu1_sq mu1**2mu2_sq mu2**2mu1_mu2 mu1 * mu2sigma1_sq cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sqsigma2_sq cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sqsigma12 cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2ssim_map ((2 * mu1_mu2 C1) * (2 * sigma12 C2)) / ((mu1_sq mu2_sq C1) *(sigma1_sq sigma2_sq C2))return ssim_map.mean()def psnr(img1, img2):# 计算PSNR指标mse np.mean( (img1/255. - img2/255.) ** 2 )if mse 1.0e-10:return 100PIXEL_MAX 1return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))if __name__ __main__:MED MED()# Baboon 3.6 0.5origindata io.imread(../img/lena_gray_512.tif)# origindata rgb2gray(origindata)print(origindata)# origindata transform(origindata)origindata np.array(origindata)MEDdata MED.detect(origindata)resultMap, countingResult HuffCode().count(origindata, MEDdata)code_dict Huffman_code(countingResult)labelInfo generatelabelinfo(code_dict, countingResult, resultMap)encrypted crypt(origindata, 3.7, 0.9)containingLabel embedinfo(labelInfo, encrypted, resultMap, )embedded embedinfo(labelInfo, encrypted, resultMap, 0110010000)huff_code, info1 extract1(embedded)# resultMap2, decrypted1, data extract_exp(embedded, huff_code, info1, 10, resultMap)# img1 crypt(decrypted1, 3.6, 0.5)print(Lena)print(Comparison of PSNR and SSIM between encrypted imagine and the original:)print(PSNR:, psnr(origindata, encrypted))print(SSIM:, ssim(origindata, encrypted))print()print(Comparison of PSNR and SSIM between containing Label Map imagine and the original:)print(PSNR:, psnr(origindata, containingLabel))print(SSIM:, ssim(origindata, containingLabel))print()print(Comparison of PSNR and SSIM between embedded imagine and the original:)print((With the secret of 0110010000))print(PSNR:, psnr(origindata, embedded))print(SSIM:, ssim(origindata, embedded))
