猿人学web端爬虫攻防大赛赛题第4题——雪碧图、样式干扰

题目网址：https://match.yuanrenxue.cn/match/4

解题步骤

看触发的数据包。
响应数据中可以看到明显的图片base64编码，去一个值解码一下，可以得到图片。（https://the-x.cn/encodings/Base64.aspx）

看来这个页面是通过图片进行回显的，直接把响应数据中的所有图片base64编码保存为图片。

import requests
import re
import os
import base64

if not os.path.exists('img'):
	os.mkdir('img')

url = "https://match.yuanrenxue.cn/api/match/4?page=1"
resp = requests.get(url)
resp_content = resp.text
pattern = r'base64,(.*?)\"'
findall = re.findall(pattern, resp_content)
index = 1
lst = []
for item in findall:
	lst.append(item)
for i in range(len(lst)):
	lst[i] = lst[i].replace("\\", "")
for i in lst:
	with open("./img/{}.png".format(index), mode="wb") as file:
		file.write(base64.b64decode(i))
	index += 1

在img文件夹里生成了55张图片，我们去跟页面上的数据对应一下，明显不是一一对应的。

这是怎么回事呢，回到页面，查看页面元素。

发现有些图片是不显示的，肯定是做了什么判断。
找数据包的initiator模块，有个request方法。

点进去看看。

代码复制出来，整理一下如下。

$.ajax({
		url: window.url, dataType: "json", async: false, data: list, type: "GET", beforeSend: function (request) {
		}, success: function (data) {
			datas = data.info;
			$('.number').text('').append(datas);
			var j_key = '.' + hex_md5(btoa(data.key + data.value).replace(/=/g, ''));
			$(j_key).css('display', 'none');
			$('.img_number').removeClass().addClass('img_number')
		}, complete: function () {
		}, error: function () {
			alert('因未知原因，数据拉取失败。可能是触发了风控系统');
			alert('生而为虫，我很抱歉');
			$('.page-message').eq(0).addClass('active');
			$('.page-message').removeClass('active')
		}
	})

可以看到class为j_key的元素不可显示，看下j_key是如何生成的。
var j_key = '.' + hex_md5(btoa(data.key + data.value).replace(/=/g, ''))
涉及到data.key、data.value、btoa和hex_md5两个变量和两个函数，看下分别是什么，在var j_key处打断点，让程序运行到这里。
再看数据包，发现跟页面响应内容中的key和value一致。
两个变量搞清楚了，再看两个函数。
btoa：base64编码函数

hex_md5：根据名字，是个md5算法，测试一下是不是没魔改过的md5算法。(进行md5之前要把base64编码后的等号给删除)

一致，说明就是个普通的md5算法。

知道怎么过滤不要的图片后，编写代码。(由于相同数字的图片的base64编码是一致的，所以可以利用一个字典先行存储)

import requests
import hashlib
import base64
import re

number_dict = {
	"iVBORw0KGgoAAAANSUhEUgAAABQAAAAdCAYAAACqhkzFAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAMTSURBVEhLrZY/TBNRHMe/bY82LeGA0Dp4NTGYqO1gdKEMwmQcGh38M5CQlGDSwWiMAwwYg2iCJsZJQ9CJ0ETsQMJgwuagLIUFXIAFXNoYcqDloLRXrq3v7v3aXktbSPCTXO77fZRvf/feu9+r5dz5iwX8R+oESigMPka6/wb2vSJUBx+1QoVD3kTz7DScb+f4YBVHA6U+ZKLPsON1IE9DtbDLP9AxEIawRgOEle4cKYSD+ZeQq8IcKquMXTbyOllPL7aiEWgSDRCmQAnahyHsiGQZruUvkIKXcObyFeM62zcOd1ylvwJ5MYDkuxA5Tjlw8A2S12iyGM7YODrujsFqfqTFCJw9L+CWyTPS3QPIBMgwKFBC9n4AaW4AdQVtQxEy1czBObkIJznAi9TDO6SLgVIYqt9QBq5YFEKCTC2momg2VXngD5bmnAc+uIqUIXQUOL/X3hJl5mHfUEgzPJ04pMUxAnN+CYeG1UlAmCLZANtqAk2kARG5m1zxQI9paWUZAsmGsArLnxOh0ZSxwCBypjwo2zQPxzCTgJ2kTtbbZ9zZ/7pRKO8WOOWqrX9iePyJiqnNGgTTuhRhgT5o5kc+JaeosDYssHbpx+OtXEziSIWqx0fqOFpg7ns2Zd24s8B1WE0V5h2mJW/ELTc0kjo2Zcm4s8ClikCIlR+si59tZpL66yosc2U8clM8bhgDkfVFUzuqR569rqXOqG7CPsOlEWiJJSraUaa/i3Q9upC94CHNCtr4WXoN+aJMzcFlakd73eGG5wnuhZHykmZ1tnx9TboYqDfNWLxoWDvqhfK+h0w1Pcg87cUBOavMmu1HMoxShuXJNNpNi7N3ewLJTyEUzIdQIIT0wgQ7EcmzxWifZMcEOZ3KY3Qwgp3RQOnbOfqJx5XGtlSOS4aK1tlHEIcXyHNsrW0dY6SBFX0ufShc70S21OwE5AR+lb+ZVfb5OVpGviGTTkHeSmBfScJqtdX55SAFoY0OsMXxISM6SvvNrshwrS7A9WoENupyv+O/oGm831sslno/RU6OsvsHu3+3yQH/AOyW6SvqnweCAAAAAElFTkSuQmCC": '0',
	"iVBORw0KGgoAAAANSUhEUgAAABUAAAAcCAYAAACOGPReAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAD0SURBVEhLY5RVUPvPQGXABKUJAA2GvzP3Mry6f5PhzfI4qBhuQNjQqD6Gzxc3Mjxzk2H4CRUiBHAYKs3wP7Gd4evhSwzPWr0ZPvBBhYkEqIaaBzL8nrmS4f3FfQzP6oIY3smwM/yFSpECkAyNY/g2q4PhhZsBwxegy/5BRZlfP2HgIdbfUIAnTD8xCGyuZ5AyW8jATqmhzD9fMwjumsUgbWPKwJu3AipKGkAydC8DZ24sg5SGDQNPei8D01OoMBkAydCnDIyHTkHZlAE8YUo+GDWU+mAEGfq46RCUhQAUGypbZwdlIcBoRFEfjBpKfUADQxkYAKYHOb9g+7HMAAAAAElFTkSuQmCC": '1',
	"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": '2',
	"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": '3',
	"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": '4',
	"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": '5',
	"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": '6',
	"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": '7',
	"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": '8',
	"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": '9', }


url = "https://match.yuanrenxue.cn/api/match/4?page=1"
resp = requests.get(url)
resp_text = resp.text
# print(resp_text)
key_pattern = r'"key": "(?P<key>.*?)"'
value_pattern = r'"value": "(?P<value>.*?)"'
key = re.search(key_pattern, resp_text).group("key")  # 获取到key
value = re.search(value_pattern, resp_text).group("value")  # 获取到value
need_md5_encrypt = base64.b64encode((key + value).encode("utf-8"))
# print(need_md5_encrypt[:-1])
md5_hash = hashlib.md5()
md5_hash.update(need_md5_encrypt[:-1])
md5_value = md5_hash.hexdigest()  # 用于筛选图片，跟该值一样的图片不会在页面显示
# print(md5_value)
td_pattern = '<td>.*?</td>'  # 找到每一个td标签，将里面的数字按顺序进行拼接
td_findall = re.findall(td_pattern, resp_text)
td_lst = []
for item in td_findall:
	td_lst.append(item)
for td in td_lst:
	img_pattern = 'base64,(?P<img_base64>.*?)\\"'  # 拿到图片的base64编码
	class_pattern = 'img_number (.*?)\\"'  # 拿到图片的hash值
	style_pattern = 'left:(.*?)\\"'
	img_findall = re.findall(img_pattern, td)
	class_findall = re.findall(class_pattern, td)
	style_findall = re.findall(style_pattern, td)
	png_base64_lst = []
	for item in img_findall:
		png_base64_lst.append(item.replace("\\", ""))
	# print(len(png_base64_lst))

	class_lst = []
	for item in class_findall:
		class_lst.append(item.replace("\\", ""))
	# print(len(class_lst))

	style_lst = []
	for item in style_findall:
		style_lst.append(float(item.replace("\\", "").replace("px", "")))
	# print(len(style_lst))

	res_png_lst = []  # 最终需要的图片
	res_style_lst = []  # 最终图片对应的位置
	for i in range(len(class_lst)):
		if class_lst[i] != md5_value:
			res_png_lst.append(png_base64_lst[i])
			res_style_lst.append(style_lst[i])
	temp = ""   # 用于拼接
	for png in res_png_lst:
		temp += number_dict[png]
	print(temp)

运行结果如下。

跟页面的数据对照一下，发现也不太对。

看看哪里还有问题，回到页面元素，在style里面存在玄机。

页面的回显内容就不是一一对应的。
接下来就要解决图片顺序的问题了，经过多次尝试，终于发现了规律。
一个td标签的宽度为45.36，一个数字的宽度为11，所以一个td标签最多只能放4位数字。

我们将45.36视为46，除以4，以11.5为基准，每一张图片按照它的顺序分别加上倍数的11.5。例如，td标签中的第一个img标签让它的style加上一倍的11.5。（要排除掉不可见的图片）

td标签中的第二个img标签让它的style加上两倍的11.5。

知道了规律之后，编写代码按照顺序拼接数字。

import requests
import hashlib
import re
import base64

# 用于存储每张图片对应的数字，每一个相同数字图片的base64编码一致
number_dict = {
	"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": '0',
	"iVBORw0KGgoAAAANSUhEUgAAABUAAAAcCAYAAACOGPReAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAD0SURBVEhLY5RVUPvPQGXABKUJAA2GvzP3Mry6f5PhzfI4qBhuQNjQqD6Gzxc3Mjxzk2H4CRUiBHAYKs3wP7Gd4evhSwzPWr0ZPvBBhYkEqIaaBzL8nrmS4f3FfQzP6oIY3smwM/yFSpECkAyNY/g2q4PhhZsBwxegy/5BRZlfP2HgIdbfUIAnTD8xCGyuZ5AyW8jATqmhzD9fMwjumsUgbWPKwJu3AipKGkAydC8DZ24sg5SGDQNPei8D01OoMBkAydCnDIyHTkHZlAE8YUo+GDWU+mAEGfq46RCUhQAUGypbZwdlIcBoRFEfjBpKfUADQxkYAKYHOb9g+7HMAAAAAElFTkSuQmCC": '1',
	"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": '2',
	"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": '3',
	"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": '4',
	"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": '5',
	"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": '6',
	"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": '7',
	"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": '8',
	"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": '9', }

url = "https://match.yuanrenxue.cn/api/match/4?page=1"
resp = requests.get(url)
resp_text = resp.text
# print(resp_text)
key_pattern = r'"key": "(?P<key>.*?)"'
value_pattern = r'"value": "(?P<value>.*?)"'
key = re.search(key_pattern, resp_text).group("key")  # 获取到key
value = re.search(value_pattern, resp_text).group("value")  # 获取到value
need_md5_encrypt = base64.b64encode((key + value).encode("utf-8"))
# print(need_md5_encrypt[:-1])
md5_hash = hashlib.md5()
md5_hash.update(need_md5_encrypt[:-1])
md5_value = md5_hash.hexdigest()  # 用于筛选图片，跟该值一样的图片不会在页面显示
# print(md5_value)

td_pattern = '<td>.*?</td>'  # 找到每一个td标签，将里面的数字按顺序进行拼接
td_findall = re.findall(td_pattern, resp_text)
td_lst = []
for item in td_findall:
	td_lst.append(item)
for td in td_lst:
	img_pattern = 'base64,(?P<img_base64>.*?)\\"'  # 拿到图片的base64编码
	class_pattern = 'img_number (.*?)\\"'  # 拿到图片的hash值
	style_pattern = 'left:(.*?)\\"'
	img_findall = re.findall(img_pattern, td)
	class_findall = re.findall(class_pattern, td)
	style_findall = re.findall(style_pattern, td)
	png_base64_lst = []
	for item in img_findall:
		png_base64_lst.append(item.replace("\\", ""))
	# print(len(png_base64_lst))

	class_lst = []
	for item in class_findall:
		class_lst.append(item.replace("\\", ""))
	# print(len(class_lst))

	style_lst = []
	for item in style_findall:
		style_lst.append(float(item.replace("\\", "").replace("px", "")))
	# print(len(style_lst))

	res_png_lst = []  # 最终需要的图片
	res_style_lst = []  # 最终图片对应的位置
	for i in range(len(class_lst)):
		if class_lst[i] != md5_value:
			res_png_lst.append(png_base64_lst[i])
			res_style_lst.append(style_lst[i])
	# print(res_style_lst)
	res_style_change_lst = []
	for i in range(len(res_style_lst)):
		res_style_change_lst.append(float(res_style_lst[i]) + (i + 1) * 11.5)
	# print(res_style_change_lst)
	res_style_sort_lst = sorted(res_style_change_lst)  # 从小到大排序
	# print(res_style_sort_lst)
	temp = ""  # 用于拼接当前数字
	for i in res_style_sort_lst:
		temp += number_dict[res_png_lst[res_style_change_lst.index(i)]]
	print(temp)

运行结果如下。

跟页面数字对应一下，完全一致。

第一页的数据完全正确，那就可以编写完整的代码爬取5页的数据了。

import requests
import hashlib
import re
import base64
import time

# 用于存储每张图片对应的数字，每一个相同数字图片的base64编码一致
number_dict = {
	"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": '0',
	"iVBORw0KGgoAAAANSUhEUgAAABUAAAAcCAYAAACOGPReAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAD0SURBVEhLY5RVUPvPQGXABKUJAA2GvzP3Mry6f5PhzfI4qBhuQNjQqD6Gzxc3Mjxzk2H4CRUiBHAYKs3wP7Gd4evhSwzPWr0ZPvBBhYkEqIaaBzL8nrmS4f3FfQzP6oIY3smwM/yFSpECkAyNY/g2q4PhhZsBwxegy/5BRZlfP2HgIdbfUIAnTD8xCGyuZ5AyW8jATqmhzD9fMwjumsUgbWPKwJu3AipKGkAydC8DZ24sg5SGDQNPei8D01OoMBkAydCnDIyHTkHZlAE8YUo+GDWU+mAEGfq46RCUhQAUGypbZwdlIcBoRFEfjBpKfUADQxkYAKYHOb9g+7HMAAAAAElFTkSuQmCC": '1',
	"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": '2',
	"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": '3',
	"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": '4',
	"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": '5',
	"iVBORw0KGgoAAAANSUhEUgAAABUAAAAdCAYAAABFRCf7AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAANLSURBVEhLrZZNSFRRFMf/48e8UZtRktFkRsiEdCIKVwaikrlJbZG0MEGDwEWUIPaBQoiIZIJGG4mikoyyIBJKW4gbs4W66WOhtlAXzixq/HzGjG/8eN0378yb+5w3qdAPLnPOmXf/nHfvOfc+U+bR4zL+M/sTrW6F71Ih/CfskAQB2xQ2SyLMU5OwPr+HuEEPRfcSddXB96QeK04BOxQyZg7pWedhJi+GfiOp7sHqwC0s7RKMkyQIwaGfzGdmLFrUAbGlFOsC+ZBwaLwfjqoSZOSeQlpw5MBR1oQjw3NIoKdCGLz+Rfgn72PRTq7khr2tFpbX4TWLwJULTM+QY5Cp/PgGlkOCEJHauYegAieosEuUbUyxU1tDy9c3SOzdQ9AAvWh7ObeOblg7u8k+GJyoA4E8FzbJi5mdhGWCnAPCiVZhM5tMRuLsAFkHJyxakcu6hWy2QcL4pGpW3IR/6AsWZ35iYV4dnvkfWBp7C/+dMvWZ3SglFRzPpuR4WZYRHAtyWsFZ2fFhVha0mPEQFkbljALSoBHO1G7V1hMIQG54Cu+FY6zsVcyhTiI/hOQswq9PfdhyUIChiW7bbWQpOLDOBANMMknppLIcpIc6KasEzrYhJIv0KGPHlo/VrlrydBvFI2CDNXfyu+s4fLkVMdMUDuKBqbcRtvr3SA69BsN/phIByjaKKPvDPQLr7THyDPjcDOuwmxNwYaNBtaKKWse7YSI7GqbO70giWyHgVJdAE431covESipuah/t6fkGMzdNsruCv+FMveuIJzMavpMlEItryIskdGaERYc9XLnYsO0kk2Mr1YG1c3XkRZLgVXc0LDoxAYF7lY28yMm20ZfIbCkij5GfD4mrxFhRPQK5jepjR52XbEW0XCuRaOxcO40/ZLP1g+Wj2tqcKHMesZuRbKVEVl506O4eHRU9WCvWTnPEz47BMqjaOlFMNLJSErVgILsSiyMPsJ3Pp5yLnfY+LHeVclm6kdLWrJWgwR1VCGmkB7+z9V2u9H5wErv3uUZiiEh5xZK5G24UfaZBxiBcaUL6lIhYiigEFLFdgjHsUkztrNEJKvz7Y6K6A76rhfA5lS+TUB1KrHQ8SBztR8LDPpgMemR/nz0HAvgL80YzEyuMQpQAAAAASUVORK5CYII=": '6',
	"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": '7',
	"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": '8',
	"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": '9', }
res_sum = 0  # 最终的和
for i in range(1, 6):
	url = "https://match.yuanrenxue.cn/api/match/4?page={}".format(i)
	# print(url)
	headers = {
		"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
					  "Chrome/130.0.0.0 Safari/537.36",
		"cookie": "Hm_lvt_9bcbda9cbf86757998a2339a0437208e=1730420198; HMACCOUNT=9443B65F99081386; "
				  "Hm_lvt_c99546cf032aaa5a679230de9a95c7db=1730420198; no-alert3=true; tk=-5370204167750759641; sessionid=ksq8egvo47lkyfvlejwm8tpv6m9njvq7; Hm_lpvt_9bcbda9cbf86757998a2339a0437208e=1730424542; Hm_lpvt_c99546cf032aaa5a679230de9a95c7db=1730450667"
	}
	resp = requests.get(url, headers=headers)
	resp_text = resp.text
	# print(resp_text)
	key_pattern = r'"key": "(?P<key>.*?)"'
	value_pattern = r'"value": "(?P<value>.*?)"'
	key = re.search(key_pattern, resp_text).group("key")  # 获取到key
	value = re.search(value_pattern, resp_text).group("value")  # 获取到value
	need_md5_encrypt = base64.b64encode((key + value).encode("utf-8"))
	# print(need_md5_encrypt[:-1])
	md5_hash = hashlib.md5()
	md5_hash.update(need_md5_encrypt[:-1])
	md5_value = md5_hash.hexdigest()  # 用于筛选图片，跟该值一样的图片不会在页面显示
	# print(md5_value)



	td_pattern = '<td>.*?</td>'  # 找到每一个td标签，将里面的数字按顺序进行拼接
	td_findall = re.findall(td_pattern, resp_text)
	td_lst = []
	for item in td_findall:
		td_lst.append(item)
	for td in td_lst:
		img_pattern = 'base64,(?P<img_base64>.*?)\\"'  # 拿到图片的base64编码
		class_pattern = 'img_number (.*?)\\"'  # 拿到图片的hash值
		style_pattern = 'left:(.*?)\\"'
		img_findall = re.findall(img_pattern, td)
		class_findall = re.findall(class_pattern, td)
		style_findall = re.findall(style_pattern, td)
		png_base64_lst = []
		for item in img_findall:
			png_base64_lst.append(item.replace("\\", ""))
		# print(len(png_base64_lst))

		class_lst = []
		for item in class_findall:
			class_lst.append(item.replace("\\", ""))
		# print(len(class_lst))

		style_lst = []
		for item in style_findall:
			style_lst.append(float(item.replace("\\", "").replace("px", "")))
		# print(len(style_lst))

		res_png_lst = []  # 最终需要的图片
		res_style_lst = []  # 最终图片对应的位置
		for i in range(len(class_lst)):
			if class_lst[i] != md5_value:
				res_png_lst.append(png_base64_lst[i])
				res_style_lst.append(style_lst[i])
		# print(res_style_lst)
		res_style_change_lst = []
		for i in range(len(res_style_lst)):
			res_style_change_lst.append(float(res_style_lst[i]) + (i + 1) * 11.5)
		# print(res_style_change_lst)
		res_style_sort_lst = sorted(res_style_change_lst)  # 从小到大排序
		# print(res_style_sort_lst)
		temp = ""  # 用于拼接当前数字
		for i in res_style_sort_lst:
			temp += number_dict[res_png_lst[res_style_change_lst.index(i)]]
		res_sum += int(temp)
	time.sleep(1)
print(res_sum)

运行得到最终的结果。

提交结果，成功通过。

posted @ 2024-11-01 17:32 死不悔改奇男子阅读(37) 评论(0) 编辑收藏举报

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猿人学web端爬虫攻防大赛赛题第4题——雪碧图、样式干扰

解题步骤

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