Python反反爬之CSS加密---样式干扰(详细教程)

Java_S

写在前面

Python反反爬系列

1. JS混淆---源码乱码
2. JS混淆---动态Cookie
3. 访问逻辑---推心置腹

这次的题目,涉及一些前端的知识,其中包括HTML,CSS,JQuery,Ajax,请各位同学做好心理准备
个人博客网站:www.syjun.vip

题目

题目网址,点我去刷题
采集这5页的全部数字，计算加和并提交结果

分析网页

老规矩,我们还是首先打开刷题网站,接着打开谷歌调试工具
查看【XHR】里面的内容

可以发现,这次传递数据的接口很特别,跟之前的题目都不一样
之前的题目涉及到的数字都会以json的格式,储存在里面,而这次虽然也是json格式的数据,却返回了一些奇奇怪怪的东西

我们接着看看【ALL】里面的内容

10张经过base64编码后的图片引起了我的注意
这些图片不就是题目中数字的图片嘛

所以,我们就可以猜测,题目中每一个四位数都是由这10张图片,通过某种规则排列组合的

下面我们查看一下页面的源码,验证一下我们的猜想

查看这张图片我们可以都出以下几个结论

• 我们的猜测成立
• 每一个td>标签就代表一个四位数
• td>标签中包含img\>标签,而每个td>中的img\>数量是不一样的;但是img>标签中,有些是有【display:none】这个样式的,这个样式的意思就是不显示此标签,也就是不显示这个图片;排除这些不显示的图片,其实td>标签中都有且只有四张图片正常显示,只不过数字不一样罢了
• 每个img>标签都设置了【left】这个样式的,【left】的作用可以改变图片在页面中显示的位置

可能,有些同学没有学过CSS的知识,理解【left】有些困难,我下面举一个例子,帮助大家理解

理解【left】

我们拿第一页中的,2914举例

观察HTML中的代码,如果不算【left】这个样式的话,真实的四位数应该是2941
前面的两位数是相同的,可以发现它俩的【left】值都是0,也就是不改变位置

而后面两个图片,一个是【left:11.5px】,另一个是【left:-11.5px】
也就是说,第三张图片向左移动了11.5px,第四张图片向右移动了11.5px
这样,第三张图片与第四张图片就更换了位置,从2941变成了2914

---

当我们刷新这个网页的时候,还可以发现,每次td>标签包含的img\>数量是不一样的,其中就有一些带有【display:none】样式的图片,给我们提供干扰信息;但是正常显示的图片肯定只有四张,而且【left】的值也是不一样的,但是正常显示出来的值肯定是一样的
(我想作者这么的做的原因应该是为了防止抓取网页源码而得出答案,这就是CSS加密吧)

所以,肯定是存在某种算法来决定正常显示的数字和提供一些干扰信息

寻找某种算法

经过前面的分析我们可以得到一个突破口【display:none】
因为在td>标签内存在含义【display:none】样式的img\>标签,而设置【display:none】这个样式大概率就会出现我们寻找的算法中

我们全局搜索一下【 'display','none' 】
这里可能就有同学要问了,为什么是搜索【 'display','none' 】不是【display:none】呢?

因为【display:none】是结果,【 'display','none' 】是过程,而执行某种算法就是一个过程
这就要涉及到JQuery里面的内容
在JQuery中有一个可以设置标签样式的函数--->css()
第一个参数就是需要设置的样式,第二个参数就是设置样式的值,也就是这样css('display','none')

---

可以看到我们要找的内容,出现在一段script>标签中,我们将其中的JS代码扣下来继续分析

分析JS代码

因为有些代码片段是有报错才执行,所以我们可以将扣下来的代码精简一下
代码中涉及到Ajax的知识,我写了一些注释,帮助大家理解

window.url = '/api/match/4'; // 设置一个url链接
request = function () {
    var list = {
        "page": window.page,// 设置页码
    };
    $.ajax({                // ajax请求
        url: window.url,    // 请求的网址,也就是上面设置的url链接
        dataType: "json",   // 预计返回信息类型:json
        async: false,       // 是否开启异步请求:否
        data: list,         // 发送给服务器的信息,也就是上面设置的页面
        type: "GET",        // 请求方式,GET
        beforeSend: function (request) {},　// 可以不管
        // 请求网址成功执行下列代码
        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')
        }
    })
};
request()

通过上面的代码,我们可以知道网页通过ajax的方式请求了[http://match.yuanrenxue.com/api/match/4]
经过前面三题的经验,我们知道这条链接包含的肯定是题目所需要的数字
而且,这条链接并没有设置什么反爬措施,我们可以直接请求,获取返回值

观察返回值,我们可以发现,【info】里面其实就是页面中的<td>标签

我们再回到ajax的代码中,看看请求网址成功执行的代码

// 首先我们需要搞清楚的是,函数里面传入的参数**data**,跟上面的data含义是不一样的
// 这里的data值的是请求成功后返回的内容
success: function (data) {
    // data.info就是获取返回内容中info的部分,也就是<td>标签里面的内容
    datas = data.info;
    // 通过JQuery的选择器,选择标签中有number的元素,并将<td>标签的内容添加进去
    $('.number').text('').append(datas);
    // 通过获取data中key和value中的值,并替换一些字符,最后经过md5算法加密,得到一串密文
    // 在密文前面加上一个【.】,并赋值给变量j_key
    var j_key = '.' + hex_md5(btoa(data.key + data.value).replace(/=/g, ''));
    // 因为密文前面加了一个【.】,所以可以通过选择器,选择有密文这个属性的标签,并将其样式设置成【display:none】
    $(j_key).css('display', 'none');
    // 选择含有img_number属性的元素,先删除所以元素,最后添加img_number元素
    // 为什么要这么做,是为了删除属性中带着的密文
    $('.img_number').removeClass().addClass('img_number')
}

我截取info中的一段代码,方便大家理解上面的代码

可以看到img\>标签中的属性部分,是有密文部分的,所以就解释了为什么会有这段代码

// 选择含有img_number属性的元素,先删除所以元素,最后添加img_number元素
// 为什么要这么做,是为了删除属性中带着的密文
$('.img_number').removeClass().addClass('img_number')

还有就是,style里面的内容,也就是【left】中的值,决定了图片所在的位置

---

通过前面的一顿分析,相信大家已经明白CSS加密到底是怎么一回事啦

1. 通过ajax请求的方式请求带有数据的链接【/api/match/4】
2. 请求成功后,将info中的td>标签添加到HTML代码中
3. 通过md5算法的一系列操作,得到一串密文
4. 匹配有密文属性的img\>标签,将其设置为【display:none】不显示
5. 接着,将属性中的密文删除
6. 最后,通过附加的【left】样式,页面中就呈现出了需要我们求和的数字

理解加密逻辑后,我们就可以编写Python代码进行获取数据啦

编写爬虫代码

---

关联数字

在文章开头,我们知道网页中的数字,其实是通过base64编码过的图片,而这些编码都是固定的
所以,我们可以写一个字典,通过映射的关系,关联数字

image_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
}

获取接口信息

我们可以根据题目提供的接口,获取到info,key,value这些数据

def get_base64_data(page):
    url = f'http://match.yuanrenxue.com/api/match/4?page={page}'
    headers = {'User-Agent': 'yuanrenxue.project'}
    response = requests.get(url, headers=headers)

    return response.json()['info'], response.json()['key'], response.json()['value']

获取j_key

通过前面ajax中的代码,我们知道,可以通过接口返回的key和value,算出一段密文,用来隐藏一些图片
虽然,ajax的代码属于Javascript的内容,但是我们任然可以用Python代码写出来

def get_j_key(key, value):
    date_str = (key + value).encode('ascii')
    date_str = base64.b64encode(date_str).decode('utf-8').replace('=', '')
    md5 = hashlib.md5(date_str.encode('utf-8')).hexdigest()
    return md5

[scode type="blue"]获取到密文后,我们就可以通过对比密文,来确定是否是真正显示的图片,从而得出结果[/scode]

获取每一个<td>标签中的内容

通过前面的分析,我们知道一个<td>标签,就是4张图片组合起来的四位数
而接口返回的info里面其实是有10个<td>标签的,如下如图所示

所以,我们可以通过正则提取每页中,每一组数字相对应的所以子图片相关数据

def parse_nums(info):
    rule = re.compile(r'<td>(.*?)</td>')
    nums_list = rule.findall(info)
    return nums_list
# nums_list列表中有10个元素,每个元素就是每个<td>标签里面的内容

完整代码

最后,我们就可以通过根据j_key和每个图片对应的密文值,确定出要被用的所以数字子图片,和相对位置的偏移量

# @BY     :Java_S
# home.php?mod=space&uid=238618   :2021/1/2 16:25
# @Slogan :够坚定够努力大门自然会有人敲,别怕没人赏识就像三十岁的梵高

import re
import base64
import hashlib
import requests

image_dict = {
    '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': 0,
    'iVBORw0KGgoAAAANSUhEUgAAABUAAAAcCAYAAACOGPReAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAD0SURBVEhLY5RVUPvPQGXABKUJAA2GvzP3Mry6f5PhzfI4qBhuQNjQqD6Gzxc3Mjxzk2H4CRUiBHAYKs3wP7Gd4evhSwzPWr0ZPvBBhYkEqIaaBzL8nrmS4f3FfQzP6oIY3smwM/yFSpECkAyNY/g2q4PhhZsBwxegy/5BRZlfP2HgIdbfUIAnTD8xCGyuZ5AyW8jATqmhzD9fMwjumsUgbWPKwJu3AipKGkAydC8DZ24sg5SGDQNPei8D01OoMBkAydCnDIyHTkHZlAE8YUo+GDWU+mAEGfq46RCUhQAUGypbZwdlIcBoRFEfjBpKfUADQxkYAKYHOb9g+7HMAAAAAElFTkSuQmCC': 1,
    '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': 2,
    'iVBORw0KGgoAAAANSUhEUgAAABQAAAAdCAYAAACqhkzFAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAALaSURBVEhLrZVPSBRRHMe/O+rOuuaUyRoyBlGQfw5lh9xTekg8qIdMKSsQFKRAD0WUROhBCIlSBD1YQsJKQSB1iRa8VRf1Up4UQiNcRVw1nXVdx3V3euP+ZnZmR9EVP/CY3/vx5sPMe+/3nu3suYsKjhGrUCxB5GErNssKERIEyDzlIYOX/MgY+4r0zm5w85ROwCRU2j1Yu+fGhi7ZG072Iau3Bc6BacrE4ejJ6EKgySyzy+yrdptpIKJ8HlbahrHZKFImjnEcIeHU6FuIlfk4U3AJObstH2L9C7hmJKTQKEBg0h7sUE/DJLT5x5FbeRWZ99kcTVFSY9wDR/kN5Pxkn6vBFyPUTjFhEPYjs6YBqYkiE/NIfTeODOqpyEUNFMUwCNmy7bNyJr5M787pfuwxhwfhh+1YhWIxtgWKGXafh6IYSQuVjssIUgxMwdFLIZGc8IEHKxV5iFL3xGg37AnzfnAtu68jUlYFueIa1i4IiFDaOfYa2XcGqRfHKnzlxVzdeepYSZVmkfXyERwfrGWnktQvc7If6T62yrxhVRKwCvX6jTet3KK8C4EiN5Y6hrEw6YV811rLhzwPRSi19dhqqmFCFzvINPzI7rwN51B8ZQ4pNNA4iNW2UgTpVOL835Fb0qz/atL7EEPNyPrm01+MutwIPaUOI3khw9Y5aTggeISu1FN8RCHm5YQX7fQ8qlDk9WpR4eRlinQhW37rDtgXpc1Yz2xrsYtLg4StCHz+iHD1IazVPQgY6hnSLzgGKGbov6y4irHY58W/T13YKS2grIHCGoTfeLHcV4V1w9V6+v0zwz2j78MuSH9uYp2SKhwbnKbvYB5hJjHOmyo7OdIC4ckP6segL5wAb7rR1Jd5dslrzSxLYQeE6/ktXbYVCmJp0YfghpRQKbWPsVVXjs0iEWGBxzalVXhZQtrMFJwj/eCHJigbY2FuFpFI7EJNvvT2wPf3NxRF1QD/AbAv8WdRHzjKAAAAAElFTkSuQmCC': 3,
    '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': 4,
    'iVBORw0KGgoAAAANSUhEUgAAABUAAAAcCAYAAACOGPReAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAIxSURBVEhLrZU/aBNRHMe/bWKexnoiaSg0FSSCaAfFwT+L7dKloUs7OqRVcHBwcZOCiCAOhk4dxAwdHFRwEEoRSjvoZNqhdDE4tC7aIYmWvOiV1941vnf51dy7S0ou6Qfu+P5+B9/7/e7e+72us+cuVHHE+EzF0jcUzlPQCjyH/itphChUdKub+bcCXv4tVQr7hsp0hmNqWXsob5ek6kWVqUxneNpPw1yfxi+nWo7Y02uIzjkPAuFUWucSrKNqvzEcoUWSAdFNbyewSxJK/SQZEN1U+0kCXaSCopvGGfZJgsv2SQbFY2rI+jqn+Y/ipbYr1dap/WYVWzfra0q98f/nkDDBcWwjj+j7WbC5Fcr6aV6pxG2oEMzAn8EbKDx+ja31jxBytTRCM7WNepVhIWRl6qKEB9tIovDsA8w7fuPWR9/QPezenQAfTmKHUg5iE32To4jkKJYc2r7G5ywiU6PoTWUQK7rKZ0lU7o9TUKN10wPyWUQfLOA0hQrz6jhs0orgporcI0TXXNUacVgkFe2ZSsI/iqT8tG162DBv23RvYICURG4K3xkVmKEZmIOkJWqXhUkrgpsm0tjJjKBCoRqRp+afkK7hMr2I6tgt0g1IXIf94h22l6dRkiPygMjXtzj5kgLCtaOeg3+fQNnR/u1pMaatRUVkYwHxyYfo9pwQTdpncnjol24o0PMpg74Rv6HCZbqC41/y6OG1QeKdpSGZO1HcRGz+FRKpyzgzlaUnXoB/3J2gmVZucHAAAAAASUVORK5CYII=': 5,
    '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': 6,
    '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': 7,
    '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': 8,
    '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': 9
}

answer_num_list = []

# 根据题目的接口,获取base64图片编码信息 每张的图的base64编码是固定的
def get_base64_data(page):
    url = f'http://match.yuanrenxue.com/api/match/4?page={page}'
    headers = {'User-Agent': 'yuanrenxue.project'}
    response = requests.get(url, headers=headers)

    return response.json()['info'], response.json()['key'], response.json()['value']

# 利用key和value计算出属性为display = none 的md5索引值
# 此函数与题目中的ajax这段代码功能一样   hex_md5(btoa(data.key + data.value).replace(/=/g, ''))
def get_j_key(key, value):
    date_str = (key + value).encode('ascii')
    date_str = base64.b64encode(date_str).decode('utf-8').replace('=', '')
    md5 = hashlib.md5(date_str.encode('utf-8')).hexdigest()
    return md5

# 使用正则提取每页中,每一组数字相对应的所以子图片相关数据
def parse_nums(info):
    rule = re.compile(r'<td>(.*?)</td>')
    nums_list = rule.findall(info)
    return nums_list

# 根据j_key和每个图片对应的密文值,确定出要被用的所以数字子图片,和相对位置的偏移量
def parse_num_info(nums, j_key):
    rule = re.compile(r'img_number (.*?)"')
    img_number_list = rule.findall(nums)
    rule = re.compile(r'base64,(.*?)"')
    base64_str_list = rule.findall(nums)
    rule = re.compile(r'style="(.*?)"')
    number_style_list = rule.findall(nums)

    # 寻找真正显示的子图的base64编码
    display_img_base64 = [base64_str_list[index] for index, img_number in enumerate(img_number_list) if
                          img_number != j_key]

    # 通过代码开头定义有映射关系的字典,解出数字
    num_list = [image_dict[i] for i in display_img_base64]
    # 寻找真正显示的子图的偏移量
    offset_list = [number_style_list[index].replace('left:', '').replace('px', '') for index, img_number in
                   enumerate(img_number_list) if img_number != j_key]

    true_order_num = get_correct_order(num_list, offset_list)
    answer_num_list.append(true_order_num)

# 此函数用于算出正确排列后的数字
def get_correct_order(num_list, offset_list):
    offset_list = [int(float(i) / 11) for i in offset_list]
    true_order_list = [None] * len(offset_list)
    for index, offset in enumerate(offset_list):
        true_order_list[int(index + offset)] = str(num_list[index])
    true_order_num = ''.join(true_order_list)
    return int(true_order_num)

if __name__ == '__main__':
    for i in range(1, 6):
        info, key, value = get_base64_data(i)
        j_key = get_j_key(key, value)
        image_base64_list = parse_nums(info)
        for j in image_base64_list:
            parse_num_info(j,j_key)
    print(f'五页数字总和:{sum(answer_num_list)}')

Java_S

icypermission 发表于 2021-1-10 20:05
有一个库 直接能在python里面执行js代码 不用你python复现算法

当然,可以这么做

萌萌哒的小白

受益匪浅

lucky_fish

我是真不懂啊

晓风残月祭

厉害了，学习一下

realyou

学习，看的眼花缭乱

寒冰流火

感觉楼主分析很深 阅读吃力 的确需要HTML CSS等预备知识啊

网络很鬼

生而为虫，我很抱歉

Java_S

网络很鬼 发表于 2021-1-8 09:06
生而为虫，我很抱歉

hhhhhhha

jianjianoo

果然爬虫需要对前后端都有所了解，学习了