douyin web端滑动，点选验证码分析

仅用在学习交流，如有侵权，联系删除。
仅用在学习交流，如有侵权，联系删除。
仅用在学习交流，如有侵权，联系删除。

当前douyin web端，请求cookie中有两个很重要的参数，一个就是s_v_web_id，他又被成为fp参数，今天简单的分析下。

1.滑动验证码

第一步，先找个页面触发出滑块

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紧接着打开控制台，找到Application，就可会看到s_v_web_id 参数了。

在这里插入图片描述

这里的s_v_web_id 值等于verify_lfhkbdvj_hWnJhmth_cNhn_4AbE_AX8t_BQl75IINjLfr，再找到network窗口，可以看到有一个https://verify.snssdk.com/captcha/get 接口，该接口主要是获取验证码的。

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在这个接口中有个fp参数，他们两个的值一模一样。

再打开网页源代码，也可以看到这个参数，说明这个参数在请求页面的时候就返回来了。
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当然可以用js 生成出来

function get_fp() {var e = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz".split(""), t = e.length, n = (new Date).getTime().toString(36), r = [];r[8] = r[13] = r[18] = r[23] = "_",r[14] = "4";for (var o, i = 0; i < 36; i++)r[i] || (o = 0 | Math.random() * t,r[i] = e[19 == i ? 3 & o | 8 : o]);return "verify_" + n + "_" + r.join("")
}

当然也可以用python 实现一下

def get_fp(self):e = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"t = len(e)milliseconds = int(round(time.time() * 1000))base36 = ''while milliseconds > 0:remainder = milliseconds % 36if remainder < 10:base36 = str(remainder) + base36else:base36 = chr(ord('a') + remainder - 10) + base36milliseconds = int(milliseconds / 36)r = base36o = [''] * 36o[8] = o[13] = o[18] = o[23] = '_'o[14] = '4'for i in range(36):if not o[i]:n = 0 or int(random.random() * t)if i == 19:n = 3 & n | 8o[i] = e[n]ret = "verify_" + r + "_" + ''.join(o)return ret

接下来就是处理图片，识别缺口位置获取距离。

第一步要做的是处理图片的空白区域，这里主要清除滑块的空白。

def clear_white(img):# 清除图片的空白区域，这里主要清除滑块的空白img = cv2.imread(img)rows, cols, channel = img.shapemin_x = 255min_y = 255max_x = 0max_y = 0for x in range(1, rows):for y in range(1, cols):t = set(img[x, y])if len(t) >= 2:if x <= min_x:min_x = xelif x >= max_x:max_x = xif y <= min_y:min_y = yelif y >= max_y:max_y = yimg1 = img[min_x:max_x, min_y:max_y]return img1

第二步是获取缺口距离。

def get_slider_point(slider_path, bg_path):"""获取滑块的缺口坐标slider_path: 缺口图片路径bg_path: 背景图片"""# 处理滑块图片img_fg = clear_white(slider_path)slice_edges_img = cv2.Canny(img_fg, 100, 200)slice_edges_color_img = cv2.cvtColor(slice_edges_img, cv2.COLOR_GRAY2RGB)cv2.imwrite(f"./img/slider_cv_.png", slice_edges_color_img)# 处理背景图片img_bg = cv2.imread(bg_path)temp_edges_img = cv2.Canny(img_bg, 100, 200)temp_edges_color_img = cv2.cvtColor(temp_edges_img, cv2.COLOR_GRAY2RGB)cv2.imwrite(f"./img/bg_cv_.png", temp_edges_color_img)# 获得距离tl = template_match(slice_edges_color_img,temp_edges_color_img, f"img/tl_.png")return tl

第三步模拟滑块轨迹

  def get_reply(self, real_width, real_height):distance = 0ans = []move_arr = []cur = 0start_time = time.time()t_dis = random.randint(520, 1500)# t_dis = 603# print("行驶距离：", t_dis)while distance < real_width:cur += random.randint(10, 20)mod = self.sigmoidx = int(mod(real_width, cur, 3500))if x > real_width:x = real_widthdistance = xans.append({"x": x,"y": real_height,"relative_time": cur})move_arr.append({"x": x,"y": real_height,"time": cur + start_time})return ans, move_arr

第四步是根据加密生成captcha body 值，代码敏感就不贴了。

dGMFEAAAMlBkSjZCa09VdHhENm9uT2VyYW40Sk5pa1VTdkdncmF2AKDN408eKbacFiODqk9oqyqVE5Mt8Q3kHYJtQ27gd6KSwr5HNYiy2tTzZL0bix/cOP4IoHFklM3WydbOXjrBPceEuPnk3IrjlIu+c97gKgH33RUTydeWIsLkDSAGfjxZKIoFMuGJ8QiW8YaUzFEaO/X/mIlFIp42MT5XxU6KBDNGAi6MEGSqYyCJG9uXp4zRP1cOagcS3UklBYlH1Y1gxfLzXdI5S5T+NpMA4qeaHLMv6tzqKInAFjz+qLa4MvnjLbaNrSGrSZQ+BBpNsPoqQfAHQxl/SdwqxzKbMtY9V6HjGvc463rWz7Y16ETsOxJWsEiyCVo+ieQ9+q7dE0PKIznY6BGTxw+8Q9AY8O+K/YqPX/J50sXBKxwSGS5tIRxPw+wDnc2HJJoEugSncaEgH8CUggPa/CP6M83KzJeNhGbCxmu+xs2Yo9q1HGhM6EfTwhXyRtS2vGW5fznOWwW8SnGbE3z3afsx7Fl9l1ljry4SkuvpRVOz2hW/l64uqm0zOz5hy8TjSBprceTi8CoiG+cssnS74XDRpEdz94V8MDrrBFL+bFNPfusxvKYW0NzT8P1VL9R8Yaz22dykReAz3sBBP0jupIT0Z0w0bo4JE7Zh4k+gZji6wXJPISluVbZ6iBoAEyGw/n/32g8dmtwCpsNwSOlLLd8uBGGC+uKNxzrMeWLuQQ/XS/pgbY8LO/w9zCIFFHg/IvlAwTHI7kwUOR4naZv9xH/g657y/+DtXFyOOjvJAO+wkP7hClPyQlvlqS18JQwzjh6yMmKlZMkCMgba9EDIQEJXeMITHny7VKIghN4C2eJvDbhhJx1Vl6Zq46MZQVw7VVON4d/rHRCkbCzagnTF3ZUXPtFLgOT7zNZ3Vc3eBnfP4XZl7moj/nwpGamV7IsohD7OMuxq80JiQQvj8uuBJmNYrXpQ1feECbYeREnCvM4TPA7MrNPU4rahLUMA7PpeR7E7yIZD6IZdrfmC0x7Zx82L244pWssl+qM4JTZtYFHsxZlGnsB57Dik7woABXnf7E6gvJJyPAkihhlnxY1tW+GciLWTKrk8VkRkNmsriKnDozFr5VRWYmf4RNu8C6ekLBj7qugKXUoLRKiYb5W2ApO0spCiqrmLCGH168+DKMc5SJPInL6MhbVdT4qfKDGIBWFsMTpX/3IcbqpPHZhD+qzrepzfqWHbiHZ9e40ydh5qMcxmRQc1xV5lnJKL3Am53Es7X1qNZW9pOIXT8ZT66d0/WwXhcl4nizmpOuX8ShwIGU75Lu3E0+7lxqPTlMeRwAC/YQA7S5msWluMs7msG4IxDNQveGDANmPOry71L4FIK18bAAPmjF1wQEaXjwK1IFFZXZ12jpFT64o5vjFGZgaGFgENkk2s6FsFLnNAC+QHHLIarC4UwH8LilERIAjPysqKv8/T/5YCHB7jSXFgpqh6cDgJCidUGc60+uTSx+ZJD3izClmgJ2