【Python 逆向滑块】(实战五)逆向滑块,并实现用Python+Node.js 生成滑块、识别滑块、验证滑块、发送短信

逆向日期:2024.08.03

使用工具:Python,Node.js

本章知识:滑块距离识别,滑块轨迹生成,验证滑块并获取【validate】参数

文章难度:中等(没耐心的请离开)

文章全程已做去敏处理!!! 【需要做的可联系我】

AES解密处理(直接解密即可)(crypto-js.js 标准算法):​​​​​​在线AES加解密工具

注意 注意 注意!!!

为了保护本作者可以持续的更新知识,此次逆向出的网站源码代码不分享,有需要可以直接找我。但作者所写出的代码(非网页源代码)会在网站最低部公示。

看此文章前请先看预热阶段的文章!!!

【Python 逆向网易易盾滑块】(实战一)预热阶段
【Python 逆向网易易盾滑块】(实战二)逆向【fp】参数
【Python 逆向网易易盾滑块】(实战三)逆向【cb】参数
【Python 逆向网易易盾滑块】(实战四)逆向【data】参数里的【d】【p】【f】【ext】参数
本篇文章讲一下【关于网易易盾滑块的小细节】和【用python如何调用js文件生成并返回数据】和成功验证滑块验证并获取【validate】值

1、关于网易易盾滑块的一些细节

仔细看,在开头我拖动箭头的时候,拼图的运动似乎有点慢【箭头的拖动和拼图是不同步的】
仔细看,在结尾我拖动箭头的时候,拼图的运动似乎有点慢【箭头的拖动和拼图是不同步的】

2、你看完视频后应该明白一些东西了吧,明白了很好,现在开始OCR识别滑块缺口的距离,识别出来的距离用于网页的这个位置【this[G4(0x8bd)][G4(0x554)][G4(0x753)]】,前面的几篇文章看过后,应该就会明白我所说的是什么地方了

3、通过OCR识别出来的距离去计算箭头的拖动距离,在文章的上方,有两个短视频,说的就是箭头拖动和小拼图的滑动是不同步的,因此我们要计算一下箭头的拖动距离,也就是真实距离。

4、真实距离就是拖动箭头的距离,有了箭头的拖动距离就可以去计算滑动轨迹了

5、在继续操作前,不知道大家有没有用过【execjs】模块,就是用来调用js文件运行的一个python模块,在这里想问大家一个问题,有没有遇到文件大的复杂的js文件,用这个模块调用的时候就会出现错误,或者是能调用成功,但返回的数据量过大时也会出现错误。今天就来帮大家来解决这个棘手的问题,而且还特别简单。

python的argv有用过吧,用命令行传参必不可少的。当然,js里也有argv功能,不懂的可以去尝试一下,我现在直接展示一下

6、既然我们有了【OCR识别距离】【滑块轨迹】【token】,那我们就可以来调用之前逆向的js文件,去生成【data】参数了

7、然后我们把代码略微优化一下,做成自动传参,来看视频

8、到这里逆向滑块就已经结束了,你已经成功的学会了如何逆向网易易盾的滑块。从逆向滑块,分析滑块,识别滑块,生成轨迹,python调用js文件,在到一体自动绕过滑块,看起来简简单单,实则有点小难度,需要有一定的耐心和思路。

下一篇文章将会讲一下如何逆向最后一个参数,并实现自动发送短信

【附上代码】由于网页原代码受保护,在次不能进行公开,还请谅解。

ocr识别滑块距离.py】滑块缺口的距离
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
真实距离.py】要拖动箭头的距离
/xfyVXlhBHuWy7m1RSdz68db8aF4Fk57ensTt3Awd3akN7xNVtIACJhvWAjZlMo1nvESOCkeZ7HazoYGi8Mt7vxBvRt7GY8sdaSWpc4mULCqP3Bn+j4mDDTrZiNMpg/mWOTytdgQ0oOtl+cm3qJkFyP0qNGbs1o2KFHXbX4+jtJMs2kjuTo0YfL6fMxxMcKsdhMvjDhxvNYGFHKDILbwlGm1Q6SKOZZMYzo2K4FQ1BSdXj/cIoFfZtBuusaD/+58AU7RJonZrihGrKVneJKjVq+QX7QnR7PAmP7J6l4GHxEcI3pIuoT6i3WR4t6eqOZ+/5OQ9zwI2UP1AvsrjRFn2weQTfPZ7Kvi+GOu0ptI5ldo7DWhX0ES8fPGwWIyycoeMP3u+E1EF2bwwzvLb46ZHjJFsYFkSPzj2Wc6RXK8SMDZAp5xJa1hsGYHY8CKeFMQw+xgx+tpF9cqBD35IUtfD6e+gWcLr65D4mBg8blVbdoDU3UteE8vuUbzDFSFd9OLivVSgwXTcDCISrz7BDASBF3dkv30CGHe4F8kmDi3NfkzKRcx92nJloObP3vemV4FwpwFPGcFQ1ybwcfnjxn898yOK2TVdOqVYj95GpfYtcYxfLeJmCsSwYp/pP8Cn3AYdiWDVCNeJqZVIBj1dUTzBFJS8zrcdSS742t6OxRWcGCzsIiu+kHOENFQhV3YP8PxfIyFHvP8eSmDxexSMdf/sGmlWiEwdv1hx7V/F99CB9oqknCo+l+x4q7WxVJG80/7fn6eFNO2FQs+B+b9JW8AmfM2es9jIBOl0csUc84MkElQj3nLD92RJ6JVtbFPCPiWbPq+QMe6c+Fg705rwCQzc/pxZuVMtlWPCLyQRk0iPfh110oMWaEKkQxxZAZbgJplwWloh6GRLJ8NNeM45+VE2snQETkOzWXLtBLOLukbfosA1gsPuaiFtjC4Rcfg9aeBWgAm4o4AY94WPZMn+xMxF9mOaP+DVSb7sWtm5ByNNFEPWC/ySJ2PnTDrkRnm/cHVc7oOdd1CW3a0qyfffTpeCQYdQ8nevou4Cayeio3vozobsh2pDj4PnaUBmoAMph/A0nX/hxHZ3/prA3uCKGj23fItjtO6JDsACGOg9tZdy+Ng0ODZ7Gm2skdbKOgTRy44mBbt2EM3RNtC9QN4hvzxO5xiyOKAF1B64E0pp0smbmQCPSw56IWiV5LYtuIURipxvNdfpwKJAhInZNFULEHO3Xmv2X5OmwHDRhS2TLqJMoukCNOPATu9z8HPkmKCiB0l0pTX+kScYW/ekZKQHbQS6BCU67pcxMuHAPOq9VxVME+ZsRbo6YvfW6vmYPfrtb7nrFvY2aaXkc+3JVjsR9s89gUs6EVOULEGoy7w1Whwz32WM3uZJJfdTStxLUKvAkVkL3r5OroKbkuI3DTTtg9NqI0eHbCSy+3gLto0VPLTc2q8r+7Bdi+P4UnJVkbkn773Izu1mQL6aTKBTVgIlnoFdqmk4M3T5Noog7rs7h3/tSg/2C2qOwao/zTbzHyPAQihTZHpy8dzeH7HEtaUeugTU78svb3CxNJrPxYgZkriB9eenBjrNnoQ9Ma0dvYpIbAA6Giz3/cHHGUST7D0aabYDxZUZF0Z44bSJj6H2pYsAagkHEWiQDR9X7/QC9/uKbu5XFQFFho/cdBowLu490WgA+mf/rvT18GaF8Ut1qDy4U8sEpnYl5EF9q5LpvQSV5fOL9p3DqE5eZP3MF3JyQATSzyEAEJN0wZ9/IP/bjYtvnaSXcQ7472hNS6eoAmtH3VcvVj/XJp1cBLPOtVxD+dPPrTWm5GtNg0NTCFTIl5DwRG7Y+oAmXerWH5HpPByoYLofEcVgYBYBn9S1tc11P/ChmS+yirevf5SDDCVZMh3563xZBcr3p/lWtXYMfGNYgIHRJuvyoRyiw5rLwRAZZ0rZ/xA3gF62+dY979eAdYBwdQu5g2IP6VZ28U+8VZMxbvaSFY45S9YZp2Z88jnWjbzTjrs+TsG29SD6Qv8I9MIDX/nATVPC1UZjJ6JQlqKx3n1BSdP1TLhjK9BMJfO4A2YQVB6f0p3Ai2PoG65Qcs1tk7EOkvH2YN6r/LvcBrTHG941N1VSNBSYi/dwtu2odlvzSCj5A2uK4HbKEIkhzEHnoX5HOMgkLrJ9Nz6zai5p2db6M0kRhX8rPXeYsYKWRxSjrF66QZ+5rCz/AmOCp8HlCAU+VhLiBAzrMTE5i+nIzmlw7HwWqc9Kb14sGLSelAMOEZK4RXpRxL1dQYx6crn/feIvzqHh+sqB9SE3GCdygTuzYXd7wG5ZWyqzlrW2EGgbcn+Jnq2M1qh6xhVE8Bod7BjVYK/y7X504TAgsPqo3K2gXpwAZnHh+FK5qAJZgyPA8bbb8EH33hVClVWnRZnVO6ivBurA/Zh1vN8ZBplcDyvrdM2zhNRv2aHBE5lYY6lYFbhp0M1ukU2nDN8j3cPeKeMFaPAqpwNxNWuFQluuiQ6x4DtgqMU9iV1qSibzIfqj5cxn7V8Hyc3NrTP/GN3YFuGQx6xTxKv3T2g0N362nS0PUY1dRCZrl4KpefE0GcZP8lNzvL3gdlpoM51gUOFlMhwEktuiBU7Ob8+YUq7+Ae/J4pctUAbXcgU+ywMesiLVQAoK+LAN4tfouninOx4j/LtOSLRqZQIqYRt+lIjAhZ4Jy49OO+Jz0QxcKVj0cULsIo+nVL9SaBMYc/mKh2gsr/0B4TXFARMCHJsO2rYebQLIVf4MX1vGnx1Df23zuNWABT5WEuIEDOsxMTmL6cjOaXDsfBapz0pvXiwYtJ6UAw4qvIvPC3hhSy9LPkG+0VinVNcLKVGQg6wlejYn2wwbBkkNy2JBi5icWtjnBtXrGSqdl4MZDwY31qgqYRWtYAen+ttkMs0XfUW71mhOoD0IB+ufWMyq4xZ6IQwI8rj0vOcwU281UB5q4MhI85sIrlLP4HRfbUZUZgtbSP4U6Duun/v1KFx7BzfIO6/Ia3/BunA20lpiDF2M/jOXD+oDNGGAOG2NRrq0WyZS22P8WrXdeoHJKf9dGxsERki+II9q+PhcFGsMUXTjTWbNdFDoN7lO88ty/ySqYNd3qLafytZl+DAaMzyIC3dGC1KiZ81DpGYoBb7mJqKSWwxzBSDwKMtfSka34z3hnzMjAsR/4eO6GAqL3BLbFn12LIeX6Z8H+auwoVlrmuZL+nQj90a3biiYYXB/mzCuEbk0l5GD0ZUsZ49XqhG1+8+QU8mLQ4d6UinzWl1b8EDb2cuvsL4PlhBzPTVRikbN8oFoYDq5dgYx6cI5DUnpKle/1pUR36fEcVlTKTAnrqK1gHC5Tz5qEHDcizE9A0W6cbu61qGb8E5hvEJS9eXK6ikf7+ER4bssP6W5RAPeFnmMNdWt1G8/xSShxoHe5gFuLfHV89Q42nVe1bXB7wjHeQ0OTpTTfYEp/1FjRxw40auf7gQz6dzdJWFrX9eI6U/VOUvLm7r2G9JniHnxkhism6d/ALgk6L0EgvwksZk04Lk6pA1Yo+p/RlQqYfitLC+u8nvbH1CQ/tWzR5Sl+ceR2M2W+SeKEbidTWXqiRTiYZJsYD01+hPAQwJerBFyOVwF4YTtFc69Yzt8l1HdH6F0SMWXudsRt1qZOInAOlRvnPPdJuYaLKuffq0xg==
滑动轨迹生成.py】生成滑动的轨迹
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
绕过滑块总代码.py
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
相关推荐
懒大王爱吃狼29 分钟前
Python教程:python枚举类定义和使用
开发语言·前端·javascript·python·python基础·python编程·python书籍
秃头佛爷2 小时前
Python学习大纲总结及注意事项
开发语言·python·学习
深度学习lover3 小时前
<项目代码>YOLOv8 苹果腐烂识别<目标检测>
人工智能·python·yolo·目标检测·计算机视觉·苹果腐烂识别
API快乐传递者4 小时前
淘宝反爬虫机制的主要手段有哪些?
爬虫·python
阡之尘埃6 小时前
Python数据分析案例61——信贷风控评分卡模型(A卡)(scorecardpy 全面解析)
人工智能·python·机器学习·数据分析·智能风控·信贷风控
前端青山6 小时前
Node.js-增强 API 安全性和性能优化
开发语言·前端·javascript·性能优化·前端框架·node.js
丕羽9 小时前
【Pytorch】基本语法
人工智能·pytorch·python
GDAL9 小时前
npm入门教程1:npm简介
前端·npm·node.js
bryant_meng9 小时前
【python】Distribution
开发语言·python·分布函数·常用分布
m0_5945263010 小时前
Python批量合并多个PDF
java·python·pdf