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How to solve slider captcha | best sliding puzzle captcha solver

How to solve slider captcha | best sliding puzzle captcha solver

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Ethan Collins

Pattern Recognition Specialist

03-Jul-2024

Have you ever felt like a master puzzle solver when faced with a slider CAPTCHA? The kind where you need to perfectly match a puzzle piece into its slot to prove you're human? I’ve had my fair share of encounters with these tricky tests, and while they can be a bit of a challenge, they’re also a fascinating glimpse into the world of web security. In this guide, I'll share my insights on how to efficiently solve slider CAPTCHAs, transforming you into a sliding puzzle CAPTCHA pro in no time.

What is Slider CAPTCHA?

As with all CAPTCHAs, a slider CAPTCHA is a verification tool used to differentiate between human users and automated bots. But unlike traditional CAPTCHAs that require the input of distorted text or the identification of objects in an image, slider CAPTCHAs are verified through a simple but effective sliding puzzle. The task is to slide a puzzle piece to a specified location in an image. This type of CAPTCHA takes advantage of human spatial awareness and dexterity, making it difficult for robots to crack. A few of the more dominant types of CAPTCHAs available today are the following

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Geetest slider

Geetest: a CAPTCHA service that verifies user identity through user-friendly slider puzzle tasks. It ensures the validity of CAPTCHA through advanced algorithms and supports multiple platforms and devices.

Datadome slider

Primarily used to protect websites and APIs from automated attacks, it detects malicious traffic through behavioural analysis and machine learning. So it is also highly secure and user-friendly.

Vision Engine

Vision Engine integrates advanced capabilities to handle slider captchas effectively across multiple platforms like Datadome, Geetest, and Shopee. So how Vision Engine works for slider captcha?

  • Image Acquisition: The first step is to obtain the CAPTCHA image files, including the background and the slider images.

  • Image Encoding: The images are then base64 encoded. If the images are obtained as data URLs, the prefix data:binary/octet-stream;base64, should be removed.

  • Submission and Recognition: The encoded images are submitted to the Vision Engine, which processes and identifies the correct sliding position within approximately 1 second.

How to automate solving slider Captcha

For those who need to solve slider CAPTCHAs efficiently, it's obvious that it's too nonsensical to go through them one by one, so automated tools are an excellent option. These tools utilise advanced algorithms and machine learning techniques to identify and solve slider CAPTCHAs quickly and accurately.

In the following sections, we'll demonstrate with code how to use Capsolver, currently the most efficient and fast solution on the market, to solve three of the most common types of slider CAPTCHA. We'll cover step-by-step code for each type, ensuring you can integrate this powerful tool into your automation processes seamlessly.

# -*- 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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"
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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()

Conclusion

In this guide, we delve into the fascinating world of slider CAPTCHAs, learn about their uses, and explore ways to solve them effectively. Slider CAPTCHAs will no longer be a problem that plagues your work if you take advantage of the third-party solution CapSolver! Also plesase remember, while utilizing automated CAPTCHA-solving methods, it's crucial to ensure compliance with the relevant website's terms of service and legal guidelines to avoid potential issues.

Compliance Disclaimer: The information provided on this blog is for informational purposes only. CapSolver is committed to compliance with all applicable laws and regulations. The use of the CapSolver network for illegal, fraudulent, or abusive activities is strictly prohibited and will be investigated. Our captcha-solving solutions enhance user experience while ensuring 100% compliance in helping solve captcha difficulties during public data crawling. We encourage responsible use of our services. For more information, please visit our Terms of Service and Privacy Policy.

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