Você já se sentiu como um mestre solucionador de quebra-cabeças quando se deparou com um CAPTCHA deslizante? Aqueles em que você precisa encaixar perfeitamente uma peça de quebra-cabeça em seu lugar para provar que é humano? Já tive minha cota de encontros com esses testes complicados e, embora possam ser um pouco desafiadores, também são uma visão fascinante do mundo da segurança na web. Neste guia, compartilharei minhas percepções sobre como resolver CAPTCHAs deslizantes de forma eficiente, transformando você em um profissional dos quebra-cabeças deslizantes em pouco tempo.
# -*- coding: utf-8 -*-
import requests
api_key = "YOUR_API_KEY"
task_type = "VisionEngine"
module_type = "slider_1"
def slideVision():
print("call capsolver...")
data = {
"clientKey": api_key,
"task": {
"type": task_type,
"module": module_type,
"image": image_slide,
"imageBackground": imageBackground,
"websiteURL": websiteURL
}
}
uri = 'https://api.capsolver.com/createTask'
res = requests.post(uri, json=data)
resp = res.json()
status = resp.get('status', '')
if status == "ready":
solution = resp.get('solution')
print("successfully get solution:", solution)
return solution
else:
print("failed to get result:", res.text)
return
def main():
image_slide = "iVBORw0KGgoAAAANSUhEUgAAAD8AAACbCxxxxxxxx"
imageBackground = "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAxxxxxx"
websiteURL = "https://xxxx.com"
result = slideVision()
if __name__ == '__main__':
main()
Using datadome slider captcha-recognition test
# -*- coding: utf-8 -*-
import requests
import base64
import re
api_key = "YOUR_API_KEY"
task_type = "VisionEngine"
module_type = "slider_1"
def slideVision(image_slide, imageBackground, websiteURL):
print("call capsolver...")
data = {
"clientKey": api_key,
"task": {
"type": task_type,
"module": module_type,
"image": image_slide,
"imageBackground": imageBackground,
"websiteURL": websiteURL
}
}
uri = 'https://api.capsolver.com/createTask'
res = requests.post(uri, json=data)
resp = res.json()
status = resp.get('status', '')
if status == "ready":
solution = resp.get('solution')
print("successfully get solution:", solution)
return solution
else:
print("failed to get result:", res.text)
return
def getImgUrl():
captcah_html = requests.get("https://geo.captcha-delivery.com/captcha/?initialCid=AHrlqAAAAAMAPaiIewl2T6gAwDWseQ%3D%3D&hash=4980A61279181687DE605B235F81B9&cid=JuCQhRul0ZumRi~7zYQcyZE4bc4qdyxscgIsByKG5ugwjg~mvvUcIhsPKNyLldpSLfQs9cwJsSsjp6hkPZxP~~OVVMRKoyPFvsIAdIHFZ6m5f~yOkx~SY7OibfCD2uBJ&t=fe&referer=https%3A%2F%2Fwww.thefork.com%2Fapi%2Fcustomers%2FpartialLogin&s=2906&e=024da721aa14a5ca04a1fcfd1a00695d6e50b8b0df4ed1fea1fb005af6cdb5b4&dm=cd", verify=False)
imgs = re.findall(r'rel="preload" href="(http.*?)" as="image"', captcah_html.text)
if "frag" in imgs[0]:
slide_res = requests.get(imgs[0], verify=False).content
background_res = requests.get(imgs[1], verify=False).content
else:
slide_res = requests.get(imgs[1], verify=False).content
background_res = requests.get(imgs[0], verify=False).content
slide = base64.b64encode(slide_res).decode('utf-8')
background = base64.b64encode(background_res).decode('utf-8')
return slide, background
def main():
# image_slide = "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"
# imageBackground = 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"
image_slide, imageBackground = getImgUrl()
websiteURL = "https://dd.prod.captcha-delivery.com"
result = slideVision(image_slide, imageBackground, websiteURL)
if __name__ == '__main__':
main()
The result is {'distance': 145}
# -*- coding: utf-8 -*-
import requests
import base64
import time
import re
api_key = "YOUR_API_KEY"
task_type = "VisionEngine"
module_type = "slider_1"
def slideVision(image_slide, imageBackground, websiteURL):
print("call capsolver...")
data = {
"clientKey": api_key,
"task": {
"type": task_type,
"module": module_type,
"image": image_slide,
"imageBackground": imageBackground,
"websiteURL": websiteURL
}
}
uri = 'https://api.capsolver.com/createTask'
res = requests.post(uri, json=data)
resp = res.json()
status = resp.get('status', '')
if status == "ready":
solution = resp.get('solution')
print("successfully get solution:", solution)
return solution
else:
print("failed to get result:", res.text)
return
def getImgUrl():
headers = {
'Accept': '*/*',
'Accept-Language': 'en-US;q=0.8,en;q=0.7',
'Cache-Control': 'no-cache',
'Connection': 'keep-alive',
'Pragma': 'no-cache',
'Referer': 'https://www.geetest.com/',
'Sec-Fetch-Dest': 'script',
'Sec-Fetch-Mode': 'no-cors',
'Sec-Fetch-Site': 'same-site',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Windows"',
}
params = {
'captcha_id': captcha_id,
'client_type': 'web',
'risk_type': 'slide',
'lang': 'en',
'callback': f'geetest_{int(time.time()*1000)}',
}
response = requests.get('https://gcaptcha4.geetest.com/load', params=params, headers=headers, verify=False)
regx = re.compile(r"geetest_.*?\((?P<data>.*)\)")
data = regx.search(response.text).group('data')
data_json = json.loads(data)
slide_res = requests.get("https://static.geetest.com/"+data_json['data']['slice'], verify=False).content
slide = base64.b64encode(slide_res).decode('utf-8')
background_res = requests.get("https://static.geetest.com/"+data_json['data']['bg'], verify=False).content
background = base64.b64encode(background_res).decode('utf-8')
return slide, background
def main():
# image_slide = 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"
# imageBackground = "iVBORw0KGgoAAAANSUhEUgAAASwAAADICAIAAADdvUsCAAAgAElEQVR4ARzBfcy37V0Y9O/rcRzn+buu+35e2oe2tKWUFiisrGCB0hU2ByKDtYDETOdEnFpZR5xMWUi2LC4zmxr9hwSzwUAXN5cNhaCMwRAcgbKBoGtKoIxCn1IKtDzt89z3df3O83jxxxxx"
image_slide, imageBackground = getImgUrl()
websiteURL = "https://dd.prod.captcha-delivery.com"
result = slideVision(image_slide, imageBackground, websiteURL)
if __name__ == '__main__':
main()
O que é CAPTCHA Deslizante?
Como todos os CAPTCHAs, um CAPTCHA deslizante é uma ferramenta de verificação usada para diferenciar entre usuários humanos e bots automatizados. Mas, ao contrário dos CAPTCHAs tradicionais que exigem a inserção de texto distorcido ou a identificação de objetos em uma imagem, os CAPTCHAs deslizantes são verificados através de um quebra-cabeça deslizante simples, mas eficaz. A tarefa é deslizar uma peça do quebra-cabeça para um local especificado em uma imagem. Este tipo de CAPTCHA aproveita a consciência espacial e a destreza humanas, dificultando a quebra por robôs. Alguns dos tipos mais dominantes de CAPTCHAs disponíveis hoje são os seguintes:
Lutando com a falha repetida em resolver completamente o CAPTCHA irritante?
Descubra a solução automática de CAPTCHAs com a tecnologia de desbloqueio automático de web com IA da CapSolver!
Reivindique seu Código de Bônus para as melhores soluções de CAPTCHA; CapSolver: WEBS. Após resgatá-lo, você receberá um bônus extra de 5% após cada recarga, Ilimitado.
Geetest Slider
Geetest: um serviço de CAPTCHA que verifica a identidade do usuário por meio de tarefas amigáveis de quebra-cabeça deslizante. Ele garante a validade do CAPTCHA por meio de algoritmos avançados e suporta várias plataformas e dispositivos.
Datadome Slider
Usado principalmente para proteger sites e APIs contra ataques automatizados, detecta tráfego malicioso através da análise comportamental e aprendizado de máquina. Portanto, é altamente seguro e amigável ao usuário.
Vision Engine
O Vision Engine integra capacidades avançadas para lidar com CAPTCHAs deslizantes de forma eficaz em várias plataformas como Datadome, Geetest e Shopee. Então, como o Vision Engine funciona para CAPTCHA deslizante?
- Aquisição de Imagem: O primeiro passo é obter os arquivos de imagem do CAPTCHA, incluindo a imagem de fundo e as imagens deslizantes.
- Codificação de Imagem: As imagens são então codificadas em base64. Se as imagens forem obtidas como URLs de dados, o prefixo data:binary/octet-stream;base64, deve ser removido.
- Submissão e Reconhecimento: As imagens codificadas são enviadas ao Vision Engine, que processa e identifica a posição correta do deslizamento em aproximadamente 1 segundo.
Como automatizar a solução de CAPTCHA deslizante
Para aqueles que precisam resolver CAPTCHAs deslizantes de forma eficiente, é óbvio que é muito ilógico passar por eles um a um, então ferramentas automatizadas são uma excelente opção. Essas ferramentas utilizam algoritmos avançados e técnicas de aprendizado de máquina para identificar e resolver CAPTCHAs deslizantes rápida e precisamente.
Nas seções seguintes, demonstraremos com código como usar Capsolver, atualmente a solução mais eficiente e rápida do mercado, para resolver três dos tipos mais comuns de CAPTCHA deslizante. Cobriremos o código passo a passo para cada tipo, garantindo que você possa integrar esta poderosa ferramenta em seus processos de automação sem problemas.
Conclusão
Neste guia, mergulhamos no fascinante mundo dos CAPTCHAs deslizantes, aprendemos sobre seus usos e exploramos maneiras de resolvê-los de forma eficaz. Os CAPTCHAs deslizantes não serão mais um problema que atrapalha seu trabalho se você aproveitar a solução de terceiros CapSolver! Além disso, lembre-se, ao utilizar métodos automatizados de solução de CAPTCHA, é crucial garantir a conformidade com os termos de serviço e diretrizes legais do site relevante para evitar problemas potenciais.