极验的破解思路

　　之前由于工作需要破解了极验验证码，在这里把之前总结的文档分享一下，目前有可能极验已经升级了，但总体思路应该还是相似的。

　　首先百度百科对极验验证的定义：极验验证是一种在计算机领域用于区分自然人和机器人的，通过简单集成的方式，为开发者提供安全、便捷的云端验证服务。与以往传统验证码不同的是，极验通过分析用户完成拼图过程中的行为特征，通过数据分析来判断是人还是机器。用户不必面对眼花缭乱的英文字符或汉字，整个验证过程变的像游戏一样有趣。

　　刚开始研究这个的时候，心想这个肯定很难搞，人家毕竟是一个公司，后来花了一个星期，慢慢的研究他的js,才发现并没有我想象的那么复杂，通过我们的分析发现极验判断的大致流程如下:

　　1.首先是刷新出图片，访问下面截图的地址，然后解析出图片名称，返回图片有两张，图一bg图片，其中图一中的图片有阴影部分，图二是slice图片。切出来的图片都是乱码，需要通过css切图拼凑出来。css切图代码见附件一.把代码中的$$$$$$$$$$$替换为我们解析出的名称，就能拼凑出如下的完整图片。

　　2.首先记录你拖动的轨迹（这个轨迹包含坐标点和时间序列，坐标点是以你点击截图中的红色框中的按钮即可），但是本次我们采用了两种方式获取坐标点，

一种手动拖几百次，采集出坐标点，因为这个图片最长为260，中间那个小块的长度就是40左右，所以拖动的距离范围一般在40-200之间，采集坐标点方法如下图一,每一个坐标点以空格隔开，具体采集点的方法看源码。

　　另一种用程序随机模拟坐标，通过这种方式模拟的点准确率不高，容易被识别为机器人。

采集到坐标点后，就需要通过Microsoft.JScript.Vsa;引擎调用JS方法userresponse产生用户行为，调用pushPoint添加坐标点，调用f()方法产线加密的拖动轨迹。(JS见附件二)
最后拼凑出提交的URL

附件一：

<head>

<title>无标题文档</title>

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附件二:

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f >= c && (f = c – 1),

f && (d = b.charAt(f)),

e %= c;

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return 0 > a && (g += ‘!’),

d && (g += ‘$’),

g + d + b.charAt(e)

};

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for (var b = [

[1,

0],

[

-1

[

-1

[

-1

[

]

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return 0

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b ? h.push(b) : (g.push(d(f[j][0])), h.push(d(f[j][1]))),

i.push(d(f[j][2]));

var tt = g.join(”) + ‘!!’ + h.join(”) + ‘!!’ + i.join(”);

return tt;

};

function pushPoint(x, y, time) {

allpoint.push([x, y, time]);

}

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return f();

};

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return parseInt(10000 * Math.random()) + (new Date).valueOf()

};

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for (var c = b.slice(32), d = [

], e = 0; e < c.length; e++) {

var f = c.charCodeAt(e);

d[e] = f > 57 ? f – 87 : f – 48

}

c = 36 * d[0] + d[1];

var g = Math.round(a) + c;

b = b.slice(0, 32);

var h,

i = [

[],

[

]

j = {

k = 0;

e = 0;

for (var l = b.length; l > e; e++) h = b.charAt(e),

j[h] || (j[h] = 1, i[k].push(h), k++, k = 5 == k ? 0 : k);

for (var m, n = g, o = 4, p = ”, q = [

10,

]; n > 0; ) n – q[o] >= 0 ? (m = parseInt(Math.random() * i[o].length, 10), p += i[o][m], n -= q[o]) : (i.splice(o, 1), q.splice(o, 1), o -= 1);

return p

};

本文链接：https://www.cnblogs.com/beststrive/p/8204118.html

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