破解极验滑动验证码

一、介绍

一些网站会在正常的账号密码认证之外加一些验证码，以此来明确地区分人 / 机行为，从一定程度上达到反爬的效果，对于简单的校验码 Tesserocr 就可以搞定，如下

但一些网站加入了滑动验证码，最典型的要属于极验滑动认证了，极验官网：http://www.geetest.com/，下图是极验的登录界面

现在极验验证码已经更新到了 3.0 版本，截至 2017 年 7 月全球已有十六万家企业正在使用极验，每天服务响应超过四亿次，广泛应用于直播视频、金融服务、电子商务、游戏娱乐、政府企业等各大类型网站

对于这类验证，如果我们直接模拟表单请求，繁琐的认证参数与认证流程会让你蛋碎一地，我们可以用 selenium 驱动浏览器来解决这个问题，大致分为以下几个步骤

#1、输入账号、密码，然后点击登陆
#2、点击按钮，弹出没有缺口的图
#3、针对没有缺口的图片进行截图
#4、点击滑动按钮，弹出有缺口的图
#5、针对有缺口的图片进行截图
#6、对比两张图片，找出缺口，即滑动的位移
#7、按照人的行为行为习惯，把总位移切成一段段小的位移
#8、按照位移移动
#9、完成登录

二、实现

安装：selenium+chrome/phantomjs
安装：Pillow

这里用的是 Chrome
Pillow: 基于 PIL，处理 python 3.x 的图形图像库. 因为 PIL 只能处理到 python 2.x，而这个模块能处理 Python3.x，目前用它做图形的很多.
http://www.cnblogs.com/apexchu/p/4231041.html

C:\Users\Administrator>pip3 install pillow
C:\Users\Administrator>python3
Python 3.6.1 (v3.6.1:69c0db5, Mar 21 2017, 18:41:36) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from PIL import Image
>>>

代码如下（增加部分注释）

from selenium import webdriver
from selenium.webdriver import ActionChains
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.wait import WebDriverWait
from PIL import Image
import time

def get_snap():
    '''
    对整个网页截图，保存成图片，然后用PIL.Image拿到图片对象
    :return: 图片对象
    '''
    driver.save_screenshot('snap.png')
    page_snap_obj=Image.open('snap.png')
    return page_snap_obj

def get_image():
    '''
    从网页的网站截图中，截取验证码图片
    :return: 验证码图片
    '''
    img=wait.until(EC.presence_of_element_located((By.CLASS_NAME,'geetest_canvas_img')))
    time.sleep(2) #保证图片刷新出来
    localtion=img.location
    size=img.size

    top=localtion['y']
    bottom=localtion['y']+size['height']
    left=localtion['x']
    right=localtion['x']+size['width']

    page_snap_obj=get_snap()
    crop_imag_obj=page_snap_obj.crop((left,top,right,bottom))
    return crop_imag_obj


def get_distance(image1,image2):
    '''
    拿到滑动验证码需要移动的距离
    :param image1:没有缺口的图片对象
    :param image2:带缺口的图片对象
    :return:需要移动的距离
    #两张图大小一样，那就通过两个for循环依次对比每个像素点的RGB值
    #如果相差超过60（threshold）则就认为找到了缺口的位置
    '''
    threshold = 60  #色差值
    left = 57    #起始位置（开始进行RGB色差判断的最小值）
    for i in range(left,image1.size[0]):
        for j in range(image1.size[1]):
            rgb1=image1.load()[i,j]
            rgb2=image2.load()[i,j]
            res1=abs(rgb1[0]-rgb2[0])
            res2=abs(rgb1[1]-rgb2[1])
            res3=abs(rgb1[2]-rgb2[2])
            # 如果相差超过60（threshold）则就认为找到了缺口的位置
            if not (res1 < threshold and res2 < threshold and res3 < threshold):
                return i-7 #经过测试，误差为大概为7
    return i-7 #经过测试，误差为大概为7
def get_tracks(distance):
    '''
    拿到移动轨迹，模仿人的滑动行为，先匀加速后匀减速
    匀变速运动基本公式：
    ①v=v0+at
    ②s=v0t+½at²
    ③v²-v0²=2as

    :param distance: 需要移动的距离
    :return: 存放每0.3秒移动的距离
    '''
    #初速度
    v=0
    #单位时间为0.2s来统计轨迹，轨迹即0.2内的位移
    t=0.3
    #位移/轨迹列表，列表内的一个元素代表0.2s的位移
    tracks=[]
    #当前的位移
    current=0
    #到达mid值开始减速
    mid=distance*4/5

    while current < distance:
        if current < mid:
            # 加速度越小，单位时间的位移越小,模拟的轨迹就越多越详细
            a= 2
        else:
            a=-3

        #初速度
        v0=v
        #0.2秒时间内的位移
        s=v0*t+0.5*a*(t**2)
        #当前的位置
        current+=s
        #添加到轨迹列表
        tracks.append(round(s))

        #速度已经达到v,该速度作为下次的初速度
        v=v0+a*t
    return tracks


try:
    driver=webdriver.Chrome()
    driver.get('https://account.geetest.com/login')
    wait=WebDriverWait(driver,10)

    #步骤一：先点击按钮，弹出没有缺口的图片
    button=wait.until(EC.presence_of_element_located((By.CLASS_NAME,'geetest_radar_tip')))
    button.click()

    #步骤二：拿到没有缺口的图片
    image1=get_image()

    #步骤三：点击拖动按钮，弹出有缺口的图片
    button=wait.until(EC.presence_of_element_located((By.CLASS_NAME,'geetest_slider_button')))
    button.click()

    #步骤四：拿到有缺口的图片
    image2=get_image()

    # print(image1,image1.size)
    # print(image2,image2.size)

    #步骤五：对比两张图片的所有RBG像素点，得到不一样像素点的x值，即要移动的距离
    distance=get_distance(image1,image2)

    #步骤六：模拟人的行为习惯（先匀加速拖动后匀减速拖动），把需要拖动的总距离分成一段一段小的轨迹
    tracks=get_tracks(distance)
    print(tracks)
    print(image1.size)
    print(distance,sum(tracks))


    #步骤七：按照轨迹拖动，完全验证
    button=wait.until(EC.presence_of_element_located((By.CLASS_NAME,'geetest_slider_button')))
    ActionChains(driver).click_and_hold(button).perform()
    for track in tracks:
        ActionChains(driver).move_by_offset(xoffset=track,yoffset=0).perform()
    else:
        ActionChains(driver).move_by_offset(xoffset=3,yoffset=0).perform() #先移过一点
        ActionChains(driver).move_by_offset(xoffset=-3,yoffset=0).perform() #再退回来，是不是更像人了

    time.sleep(0.5) #0.5秒后释放鼠标
    ActionChains(driver).release().perform()


    #步骤八：完成登录
    input_email=driver.find_element_by_id('email')
    input_password=driver.find_element_by_id('password')
    button=wait.until(EC.element_to_be_clickable((By.CLASS_NAME,'login-btn')))

    input_email.send_keys('18611453110@163.com')
    input_password.send_keys('linhaifeng123')
    # button.send_keys(Keys.ENTER)
    button.click()

    import time
    time.sleep(200)
finally:
    driver.close()

案例：

1. 破解博客园后台登陆

from selenium import webdriver
from selenium.webdriver import ActionChains
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.wait import WebDriverWait
from PIL import Image
import time

def get_snap():
    driver.save_screenshot('full_snap.png')
    page_snap_obj=Image.open('full_snap.png')
    return page_snap_obj

def get_image():
    img=driver.find_element_by_class_name('geetest_canvas_img')
    time.sleep(2)
    location=img.location
    size=img.size

    left=location['x']
    top=location['y']
    right=left+size['width']
    bottom=top+size['height']

    page_snap_obj=get_snap()
    image_obj=page_snap_obj.crop((left,top,right,bottom))
    # image_obj.show()
    return image_obj

def get_distance(image1,image2):
    start=57
    threhold=60

    for i in range(start,image1.size[0]):
        for j in range(image1.size[1]):
            rgb1=image1.load()[i,j]
            rgb2=image2.load()[i,j]
            res1=abs(rgb1[0]-rgb2[0])
            res2=abs(rgb1[1]-rgb2[1])
            res3=abs(rgb1[2]-rgb2[2])
            # print(res1,res2,res3)
            if not (res1 < threhold and res2 < threhold and res3 < threhold):
                return i-7
    return i-7

def get_tracks(distance):
    distance+=20 #先滑过一点，最后再反着滑动回来
    v=0
    t=0.2
    forward_tracks=[]

    current=0
    mid=distance*3/5
    while current < distance:
        if current < mid:
            a=2
        else:
            a=-3

        s=v*t+0.5*a*(t**2)
        v=v+a*t
        current+=s
        forward_tracks.append(round(s))

    #反着滑动到准确位置
    back_tracks=[-3,-3,-2,-2,-2,-2,-2,-1,-1,-1] #总共等于-20

    return {'forward_tracks':forward_tracks,'back_tracks':back_tracks}

try:
    # 1、输入账号密码回车
    driver = webdriver.Chrome()
    driver.implicitly_wait(3)
    driver.get('https://passport.cnblogs.com/user/signin')

    username = driver.find_element_by_id('input1')
    pwd = driver.find_element_by_id('input2')
    signin = driver.find_element_by_id('signin')

    username.send_keys('linhaifeng')
    pwd.send_keys('xxxxx')
    signin.click()

    # 2、点击按钮，得到没有缺口的图片
    button = driver.find_element_by_class_name('geetest_radar_tip')
    button.click()

    # 3、获取没有缺口的图片
    image1 = get_image()

    # 4、点击滑动按钮，得到有缺口的图片
    button = driver.find_element_by_class_name('geetest_slider_button')
    button.click()

    # 5、获取有缺口的图片
    image2 = get_image()

    # 6、对比两种图片的像素点，找出位移
    distance = get_distance(image1, image2)

    # 7、模拟人的行为习惯，根据总位移得到行为轨迹
    tracks = get_tracks(distance)
    print(tracks)

    # 8、按照行动轨迹先正向滑动，后反滑动
    button = driver.find_element_by_class_name('geetest_slider_button')
    ActionChains(driver).click_and_hold(button).perform()

    # 正常人类总是自信满满地开始正向滑动，自信地表现是疯狂加速
    for track in tracks['forward_tracks']:
        ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform()

    # 结果傻逼了，正常的人类停顿了一下，回过神来发现，卧槽，滑过了,然后开始反向滑动
    time.sleep(0.5)
    for back_track in tracks['back_tracks']:
        ActionChains(driver).move_by_offset(xoffset=back_track, yoffset=0).perform()

    # 小范围震荡一下，进一步迷惑极验后台，这一步可以极大地提高成功率
    ActionChains(driver).move_by_offset(xoffset=-3, yoffset=0).perform()
    ActionChains(driver).move_by_offset(xoffset=3, yoffset=0).perform()

    # 成功后，骚包人类总喜欢默默地欣赏一下自己拼图的成果，然后恋恋不舍地松开那只脏手
    time.sleep(0.5)
    ActionChains(driver).release().perform()

    time.sleep(10)  # 睡时间长一点，确定登录成功
finally:
    driver.close()

2. 修订版博客园后台登陆

from selenium import webdriver
from selenium.webdriver import ActionChains
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.wait import WebDriverWait
from PIL import Image
import time

def get_snap(driver):
    driver.save_screenshot('full_snap.png')
    page_snap_obj=Image.open('full_snap.png')
    return page_snap_obj

def get_image(driver):
    img=driver.find_element_by_class_name('geetest_canvas_img')
    time.sleep(2)
    location=img.location
    size=img.size

    left=location['x']
    top=location['y']
    right=left+size['width']
    bottom=top+size['height']

    page_snap_obj=get_snap(driver)
    image_obj=page_snap_obj.crop((left,top,right,bottom))
    # image_obj.show()
    return image_obj

def get_distance(image1,image2):
    start=57
    threhold=60

    for i in range(start,image1.size[0]):
        for j in range(image1.size[1]):
            rgb1=image1.load()[i,j]
            rgb2=image2.load()[i,j]
            res1=abs(rgb1[0]-rgb2[0])
            res2=abs(rgb1[1]-rgb2[1])
            res3=abs(rgb1[2]-rgb2[2])
            # print(res1,res2,res3)
            if not (res1 < threhold and res2 < threhold and res3 < threhold):
                return i-7
    return i-7

def get_tracks(distance):
    distance+=20 #先滑过一点，最后再反着滑动回来
    v=0
    t=0.2
    forward_tracks=[]

    current=0
    mid=distance*3/5
    while current < distance:
        if current < mid:
            a=2
        else:
            a=-3

        s=v*t+0.5*a*(t**2)
        v=v+a*t
        current+=s
        forward_tracks.append(round(s))

    #反着滑动到准确位置
    back_tracks=[-3,-3,-2,-2,-2,-2,-2,-1,-1,-1] #总共等于-20

    return {'forward_tracks':forward_tracks,'back_tracks':back_tracks}

def crack(driver): #破解滑动认证
    # 1、点击按钮，得到没有缺口的图片
    button = driver.find_element_by_class_name('geetest_radar_tip')
    button.click()

    # 2、获取没有缺口的图片
    image1 = get_image(driver)

    # 3、点击滑动按钮，得到有缺口的图片
    button = driver.find_element_by_class_name('geetest_slider_button')
    button.click()

    # 4、获取有缺口的图片
    image2 = get_image(driver)

    # 5、对比两种图片的像素点，找出位移
    distance = get_distance(image1, image2)

    # 6、模拟人的行为习惯，根据总位移得到行为轨迹
    tracks = get_tracks(distance)
    print(tracks)

    # 7、按照行动轨迹先正向滑动，后反滑动
    button = driver.find_element_by_class_name('geetest_slider_button')
    ActionChains(driver).click_and_hold(button).perform()

    # 正常人类总是自信满满地开始正向滑动，自信地表现是疯狂加速
    for track in tracks['forward_tracks']:
        ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform()

    # 结果傻逼了，正常的人类停顿了一下，回过神来发现，卧槽，滑过了,然后开始反向滑动
    time.sleep(0.5)
    for back_track in tracks['back_tracks']:
        ActionChains(driver).move_by_offset(xoffset=back_track, yoffset=0).perform()

    # 小范围震荡一下，进一步迷惑极验后台，这一步可以极大地提高成功率
    ActionChains(driver).move_by_offset(xoffset=-3, yoffset=0).perform()
    ActionChains(driver).move_by_offset(xoffset=3, yoffset=0).perform()

    # 成功后，骚包人类总喜欢默默地欣赏一下自己拼图的成果，然后恋恋不舍地松开那只脏手
    time.sleep(0.5)
    ActionChains(driver).release().perform()

def login_cnblogs(username,password):
    driver = webdriver.Chrome()
    try:
        # 1、输入账号密码回车
        driver.implicitly_wait(3)
        driver.get('https://passport.cnblogs.com/user/signin')

        input_username = driver.find_element_by_id('input1')
        input_pwd = driver.find_element_by_id('input2')
        signin = driver.find_element_by_id('signin')

        input_username.send_keys(username)
        input_pwd.send_keys(password)
        signin.click()

        # 2、破解滑动认证
        crack(driver)

        time.sleep(10)  # 睡时间长一点，确定登录成功
    finally:
        driver.close()

if __name__ == '__main__':
    login_cnblogs(username='linhaifeng',password='xxxx')

用类封装的版本 svcr

import time
import random

from selenium.webdriver import ActionChains
from selenium.webdriver.common.by import By
from PIL import Image


# def simulate_reaction(func):
#     """模拟人类的反应时间"""
#     from functools import wraps
#
#     @wraps
#     def inner(self, *args, **kwargs):
#         time.sleep(random.uniform(0.2, 1))
#         ret = func(self, *args, **kwargs)
#         return ret
#     return inner


class SVCR:
    """识别滑动验证码   极验验证"""

    def __init__(self, driver):
        self.driver = driver
        self.get_full_img = True

    # @simulate_reaction
    def run(self):
        """执行识别流程"""
        # 1. 点击按钮开始验证
        self.click_start_btn()

        # 2. 根据验证类型验证
        return self.judge_and_auth()

    def judge_and_auth(self):
        """判断验证类型并执行相应的验证方法"""
        if True:
            return self.auth_slide()
        else:
            pass

    def auth_slide(self):

        def get_distance(img1, img2):
            """计算滑动距离"""
            threshold = 60
            # 忽略可动滑块部分
            start_x = 57

            for i in range(start_x, img1.size[0]):
                for j in range(img1.size[1]):
                    rgb1 = img1.load()[i, j]
                    rgb2 = img2.load()[i, j]
                    res1 = abs(rgb1[0] - rgb2[0])
                    res2 = abs(rgb1[1] - rgb2[1])
                    res3 = abs(rgb1[2] - rgb2[2])
                    if not (res1 < threshold and res2 < threshold and res3 < threshold):
                        return i - 7  # 经过测试，误差为大概为7

        def get_tracks(distance):
            """
            制造滑动轨迹

            策略：匀加速再匀减速，超过一些，再回调，左右小幅度震荡
            """

            v = 0
            current = 0
            t = 0.2
            tracks = []

            # 正向滑动
            while current < distance+10:
                if current < distance*2/3:
                    a = 2
                else:
                    a = -3
                s = v*t + 0.5*a*(t**2)
                current += s
                tracks.append(round(s))
                v = v + a*t

            # 往回滑动
            current = 0
            while current < 13:
                if current < distance*2/3:
                    a = 2
                else:
                    a = -3
                s = v*t + 0.5*a*(t**2)
                current += s
                tracks.append(-round(s))
                v = v + a*t

            # 最后修正
            tracks.extend([2, 2, -3, 2])

            return tracks

        # 1. 截取完整图片
        if self.get_full_img:
            time.sleep(2)            # 等待图片加载完毕
            img_before = self.get_img()
        else:
            img_before = self._img_before

        # 2. 点击出现缺口图片
        slider_btn = self.driver.find_element_by_class_name("geetest_slider_button")
        slider_btn.click()

        # 3. 截取缺口图片
        time.sleep(2)            # 等待图片加载完毕
        img_after = self.get_img()

        # 4. 生成移动轨迹
        tracks = get_tracks(get_distance(img_before, img_after))

        # 5. 模拟滑动
        slider_btn = self.driver.find_element_by_class_name("geetest_slider_button")
        ActionChains(self.driver).click_and_hold(slider_btn).perform()
        for track in tracks:
            ActionChains(self.driver).move_by_offset(xoffset=track, yoffset=0).perform()

        # 6. 释放鼠标
        time.sleep(0.5)  # 0.5秒后释放鼠标
        ActionChains(self.driver).release().perform()

        # 7. 验证是否成功

        time.sleep(2)
        div_tag = self.driver.find_element_by_class_name("geetest_fullpage_click")
        if "display: block" in div_tag.get_attribute("style"):
            '''判断模块对话框是否存在，如果存在就说明没有验证成功，"display: block"，重新去验证'''
            self.get_full_img = False
            setattr(self, "_img_before", img_before)
            return self.auth_slide()
        else:
            #如果验证成功"display: none"
            time.sleep(1000)
            return True

    # @simulate_reaction
    def click_start_btn(self, search_style="CLASS_NAME", search_content="geetest_radar_tip"):
        """找到开始按钮并点击"""
        btn = getattr(self.driver, "find_element")(getattr(By, search_style), search_content)
        btn.click()

    def get_img(self):
        """截取图片"""
        div_tag = self.driver.find_element_by_class_name("geetest_slicebg")

        # 计算截取图片大小
        img_pt = div_tag.location       # {'x': 296, 'y': 15}
        img_size = div_tag.size         # {'height': 159, 'width': 258}
        img_box = (img_pt["x"], img_pt["y"], img_pt["x"] + img_size["width"], img_pt["y"] + img_size["height"])

        # 保存当前浏览页面
        self.driver.save_screenshot("snap.png")

        # 截取目标图片
        img = Image.open("snap.png")
        return img.crop(img_box)

使用类

from selenium import webdriver

from svcr import SVCR


def auth():
    driver = webdriver.Chrome()
    # browser.get(url)
    driver.get("https://passport.cnblogs.com/user/signin")  #请求页面
    driver.implicitly_wait(3)
    # 第一步：输入账号、密码，然后点击登陆
    input_name = driver.find_element_by_id('input1')  #找到输入用户名的框
    input_pwd = driver.find_element_by_id('input2')  #找到输入密码的框
    input_button = driver.find_element_by_id('signin')  #找到按钮
    input_name.send_keys("name")#博客园的账号
    input_pwd.send_keys("pwd")#博客园的密码
    input_button.click()  #进行点击
    return  driver

def main():
    driver=auth()  #进行验证，
    _auth = SVCR(driver)
    _auth.run()

if __name__ == '__main__':
    main()