Blog
Como resolver o captcha deslizante | melhor solucionador de captcha de quebra-cabeça deslizante

Como resolver o captcha deslizante | melhor solucionador de captcha de quebra-cabeça deslizante

Logo of Capsolver

CapSolver Blogger

How to use capsolver

03-Jul-2024

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 = "iVBORw0KGgoAAAANSUhEUgAAAD8AAACbCAYAAADV0kMnAAAAAXNSR0IArs4c6QAAGpxJREFUeF7tewd4XNWZ9ntum6pRL1ax3G2BC6YtLpJsNiHgOMaWTDFeArsbQgjhp2R5lvzJElj2T1iyZDcsNoaAMU4wsbFFiSnGFGvcccPGlovcLdnqo9G0O3PvPWefc+8dSWaT/AzoSYI881iPZkZnyvu97/d+33fuMcEFfCMXMHakwV+o7KeZTzN/AUYgLfsLkHQTcpr5NPMXYATSsr8ASU8bXlr2adlfgBFIy/4CJD3t9mnZp2V/AUYgLfsLkPSBd3u2fKIHvqxJlLKJYGQyI6QYYFEC1gQIzYyyQ9J8/9t/LcEeMNmz1VWTDYIfd8aEoo4oCgKqUBjT4JMEaE4JIbeMcKGXNuc56V5REBaRmg37/9JBGBDw2qvTZyQg/uiTVnLF9mYxu6GdoLFTxOkeAV4FKPcxlGcyVJcbGJFNW0bl0INe2Vgu1W5c9pcMwJcGr6+umtWjkQf3tQl/s+xT0fXhSdHCwwQQ0vf2AgQQEFxdrqOmImFcVWJsypCNn8vzN677SwXgS4FndVMLwpq0fOtZ4W+f2S1JH5/jwIkJk/+YO6SEgJmw+z5qQgHFA1eqsSuG6PUehf6QzN3Y8JcIwJcCb9RVPbS7Tbh7RYNQ+rsGCZzdJGDCLODgYeinAOs+wTeGa/j2+Hj3lcXacrnGf+9XCjx7Y9rYzqj87MYzqH7gQxmqwYFbsRQ5z8SWv82+xXyfGvjffzEjhL8Zou3MlqKzvbdsb/1zB+ALM6+vqfzOwS7xn5/8WBq17qTUy64JkVmMc6wC4cz3KYI/yQgxA7SgIoabK9TjFTnag1KNv+4rA95YU/mj3W3SD+5Y5yhuixAIhMNKsmuD5yHgMidcCfzHUgePjcCAi/N0/OiqSOCKwvhSudb/T18d8HWVT245K//jne8qmaEEZ5lDtxlmHKN13wJuOb0VAAJqg/fIwFNXBxPTS9XfSTX+27464F+rXr7htHTT99Y5lDjPdyKAYzb55w5vK8HSglXorDUEom2ARR6Kx6uCoSlDYkvFGv99Xxnw+prKp3a2ygse3uTMOxyQzwNuguB5YOc/h85zn/YzPP63cTkGHr4q0HF5UfxpcZ7/0a8O+Lqqbx8NSA/9ep9SsbrR2cu4hY+zn/QAW/I81032rZrPU2H+6DBuHBNpmpgX/4VYU//UVwZ8fPW0iyOavOiNY0r1o1s9ltvbdd0UOi/0/Lepf0vuVkQsb8h0UPzblA5ckp/YWuim3yfzNnzylQHPHoFgTKhasbdDrvzlLnfx1hYHGEs6uuXyVtmzzY8HIFnyIGLBmB58a0S4ZXJedLFYu/GxPzdwS59f4qatqZ5JGR7c3qJUPb7T6zkcUGAmP7FKn9XiWmWP13arAhCMy9Jw36VdxhUFsY+8EnsiobOW//U1BIMqEBOqltCcbjEBQU4gFtIAJUFu3Br7El+796Xngdfrqn//Rd60J04ytrY4pi/ZlyEeCsi9La4VXbvz48AJwcScBL43sRsZMu3IUox2QYQhgFGBwBDNH/u+IFCJwAB0KggwRDDzsSQynb+vVFP/rS/yXfu/phc8B/7haWm27dHnaaJ3KOld3T9m54vn1UYPPmpy2h2fXfvt4cYhAteUReGQAElgkAVAJoBIGGQR5mPuDrJI4ZXdkEUCTQ9DFnVIhEASKBSRYUZpZO0Ag696/aPT8vV3rXeD2X25ZWKibWSWg/ev4wL6+nczomaht6Rupbcte/OB5fpmh8dzwlxrqSI57DDGwBsErpYMKRcQBHTH2sCggXHLZMDiq5sxszT0hlTjnzuAzFfVfXRanvf99z2gvC4Lgm1WkvUZ/Pv2a1H7T2r9FvRNcGadtw3Q7PX7ATcDlez3+wYibpg8AIQJyJBzwAhDUO0AAwXMbwUsmtmMmSXh16Xa+nkDB35N1er6Znnu99/3iAYVLbc2mbfBJ4HYzYvla0nJm5E5L9eth9ZQk8x7/pw129utb6/f2qbImAleIBIy5CxoegJhrdt8jsGAKBAsntFMZ5SE3pBq/TUDCL76VX+zNPfuD7ySTkVrCDEbFdHs1swWtXc6492adbMxW3fMfxawPne3n+OBshWQnPK4ypOqMRXPX8fBMxleJRNxLYaYFrIkz71AoHh6ZrNRbTLvnz9w4OuqVvrPKDX3fJQhJUzw9hBi5yQHLtpeYAmwH3A7LzioZFD65nle9pLSTiolybwF1vQBDp5ZAheoAq8jE2oigrgRtlKBMEgixeLqZr2qJMJlf8MAgq9+ZWOTXHvvhgxZ1bnU7QGFs04s1q26bddsezTtzXczK/tvVvQBtoyuDzAPjAXaMhNKrZDx5wzKIBEnPHImVC18HnjO/KIZzXp1abhOrPHfNHDg11S9vKnZMf+eDT5FNSyHT87fPP/NLSozFZLg+5sWF6WtfHubykyXXl9IGp7tnCbTVn7zFyZZ5/KmoJCYE245Ewk9AjXJPKNwScBTM5q0ypJwnVTrv3kgwf9201nH/Pv8mY6IZue87dAmeLPk9bFuytPcobXyu5fHpAmaKdLX7ibz3ab4PNAW6/wpCoNx8A64ZB80I4a4HgLlPgAGRTJzXqsqjq6RausXDCD4yuWbzrlufKA+0xGywZtuzniuWyDM3pwA1M7hvubHzuVkSettZe2KYJe13hShNuu21PtynvNOITIFLikDCT1myd6s8QxumeJXM84mqkrCq6Va/8KBA19XtWzzWedNP/RnO4NaX84P92kYmsE7K+BojxMnQ0rffh0PwnnA+pe/z9Rxu4ZbRmkbZq/0GRi1tEPB3V6AQpzQDRUa1QEqATQBxUWxeMaZeGVJhIP/u4EE/8Lms85bfrgxxzks08DsYRGUenmesVCGg3XyvjuUINmBuOg73O2UtrR48Gmn9zOf30/m/SpAsjQkTe6z4HnnlvQAzjwxJMhwgFINqqHCoLwdNpApxPAf1c3xmaOMVeK8+m+bgaybMd4gdCoBmc4Ysvm1QcZwRhBwRgDbT+Zt/OCPBamvt19T9fyWc85b9nUqrvG5Wnxkpv5pnjPRqIjkuEFxmg8ghJGLGTA6mBB9x4NKxdpTmfmrjub0tbHnNT79dmztZiBp8GYPYEu+1/Rsx6eMmhIXdAdAKRJMAxEYSJwh0XwcS2/X1KtHGysNwhYLjNx9LiJXdMaE/C5VKEoYcIqA7pJoj1dhwdIM/XSWw2hgVHhFvqF+42eD0G+wqXxu6zn3wgyFdo7L0rY7JP1xMm/Trs++gK26SNGRfSURxbuOBp2XbW7xjH2hIR+BBJ/m/gDz/bq4PvDJ+m7/7ucBXPYwdAgJB6gqcPwQoKLt2BlkemS8dGdMrRoWez+sCe49rY4pp4Oia2eLA1vOOdAcEuGRDAzzaRiZlcDXh8UwMpuerMgxDssyrZPm+Z/rj6c/80s649KEPKe+RYD6OKn5uPNP5RSruzJXh+ufO1Vpen1TxpT/t7sYmt0cJWXeV+qSOW6VOkZob5035c7z3cx/nvQEiAPRdh2JUAKK5EK4+wwyXF74cj145pam2ISi6HYO/DcNLsf6E26AiqDM6E0dZt93iAauHx3HvHHx6FUl2ianaCyRaje9lsTVB3511TNEYEcEvXMRubEh8XnNRFtT9Vhj0HXTS4fyR795Mtsyw96elxtgX7PTx7wN3ixvHDy1DY+AxWXoAYpYaxjxYBtc7gzEEiGUFo9BlsuFh2fv0HWB0Kf35Ch7u6wSydtCrhjLN/h7U7NxMjtGSvH1kXHcdWk8OLXM2OIQ9J+Rms2bkh2HiVOvq1oo7vO/Qh7p7VA/F3624rI8w+FZsrnFd91jO0vdzTHFnG2TTU7vNNfXyMNihm9PWLWdf1kzzxMi9G4BQ3MKwUKAFjNwqvkADKpiaNkYXDS8FDeMewO/XO/DoXARDFcccMbBBP5+htkPcM/gv5PA+W+darh1QgK3TjDappQY74u1/r87n6LPBfUPL9LXVM/riEsPLtlfOGXlsUJrJ6uvy+/dLbPc3uriQAUQnuv8MWfLYBCiDhjtEmbNrMLebQfQ0noCufkyjp9sxMSKy3H5hNFQut/CSelaELcL6zZuguGMQHSrMMx35e/VBzzJvsF08/ssnZ3AtDL6cZmP3U5q/Ae/1B5eMhRnVk1xDZGUVasa86r/bXdphiXmfuOufT9Z6gzT2RkITbJEIGgy5JAPRUo5rpk5EpvqDyMQPIC/v30hfr96NyJdmXD7GDq7W1B57WRcV3MlfvXsStTv2ArJ1w1d4sDt4ciWvEEtNVhKo/juZA23T6InLxnCHpFq/C8NCHg7bZbtaMuYc8eG4dl8PyCJ3QIsnGdwfHgxd0q47g0CgQIkIkEIZCKbDIOaaEbFxQWYNLkEH7x9BJGAA2pUB5Gc0BlQWOrELd+Zhn1HTuCVN99EXO4CVaKWgj4jffOxYYGvyDPw5DU0PHM4e0mq8f9gwMAbr1X/654Ozx0Pby8tOtHj6GWeYzdHXZsNa2qnoPwSlyaBhSm0UA9IlMGh5SLLWwxF9EJQuiErAUArQDzsgcvtQ3coDgNRxGkHahdMgyF5sfz1FQihBZDjlvDNsfj8nGfUMJWW6TDwci2Nfm04WSHW1N8xYOC11VUP7W3z/J9fbB86ZF/AAyZzOlmv8VsubE8wXI6aAr3bQKy1A6G244AWRV5WORSlDEW5F+HQ0Xdw2eXFuLHmVry+5ihOnAiBCQyq1gFKVGRkSwglYgiwJjCfBkjENjzL8ZO5bzVRVhksyaB4YQ4LzhzOlvFrgwMGXq+rXrrtmHfBncuznR0JJzLyMkD4dq0CSE6+D6qDUsPcE2CaiEQPQ6Q1gkjncaihMxg6pAT33XsPWttjeOWlevSED2PWrG/g/gfvwIf1+/DiC9sQ7gmC0iiKy4twxfQqrPOvRZt6GNTBA22xbvSTvRUAbq7W38bkUjwzGx2V5XhWrPH/ZEDAs1VTclQhY2UnmfK1c4X3Y+feY1j+8ivY33AIntxC+PLzQEUKyUEgyyLCARWRrh6ogTAUxKHFApgz51o8/MgdEBXg1gU/xt5PtmHc2Mtxz/33oWLiCPg3HsO/PvJzlJQUY/bc29BwfD92NLwDwxGBRmKWhfCEsllP5r/FvAX+hvEMD1WRQxfn0eVi7cafDwh4fkojEMu9a0PDhEtXfzwOYypGYuLkHCheit2fnkJDYyOaWnvQ0hEEd3o9EgSNx5HlygZNRJBIBBGNBdCjNoGyEAiTkZ9bjFsX3oWubhn7jzRg7EXjceToAZRJu7EnOAFhdCLGmiCIvIjZUk82Njb7lvlZ4zDfAnu+BsacsVjvlOh/8IGnX2//xZqceF1Vhcjw70dah1a/ffgGX4KNgqrqON10FqFYF9wZBLkFGYDsBJMcOHnqJHbv2gKnJCMnIwvB7jOYOn0SJFnGe++/hbbOY3DIXvh82Rg+ogwtbV0419aBO793P1RqYKr0PFbu9WBbhwNEVEEZTyfBam+TRtfb6fEOzwrMrLEUD83A8UlFWC9+6v8+b+b69faViwlwRKzd+F+ft99hb49yGLHiRQebs65Ztbm8bN3ufPi8XgwpHILi4qEgTERnewe6uroRUVWITieIQ0bx8BIc2L8NZ08fxOhR2bj33ptx9GgQv/rvZxDTuyDRHGRl5SMYakJ792EYVMATTyxFZmEpik4+AKAbT25x42A77+k16Pyqlj0c9bW4XO2W8U0qNvBgNeLVI8hGn4z/S2rrd5zX3hp1Vc9EVGeFyxHbLhn6v5Mbt3b9qSCoq6ePkAThgbMBX9W2IyUTFq+fDEIMGLqKaLQHkgAoooIhRSVwu73QNApZcSIrNw+lY0pRPsKNQPcRvPfu29i6ZQdCoR4k9CDuvfcn2LljF26+eRa2bd+O36x4CbLkxfya7+CSad9CleNnyMHBXccCwsQ3GyT594ckRBK63c4m+/ukwwMzRxm4eyoJX16K7V6Z1Im19Yv/92BTV/ncobPDF2a5Y81FmW27GOjTktG547NDTuDFS7IyfL5ZOhVvPd2Rc/GJtpyyl/2T0Bbkmw/2hwuApDAktDjUaNQ8nJabnQdmGAiHg3D5BDjdBm7/7tUYNboUTz7xKjZv2gU13o3cnFEACWP8pOFoOtuOvft3glAZ2d4RqL3tB5g/bAnGZ336LmOEHOwQxh3vJOXrG0Uc7WA43gX0xIChmRSXlhq4aijDmAJ0X1mGbbLAlkm1/pV/bKR9vilYsaApONY9LPdgIM91erckqjECeoQR0kAM5mICJhpMKosb2fkhvXh8U3SK40RoGuIJHZFIDKGeCAKBIDo6Agj09CAS1aAoXoiyA3E9ivwiF6Kxbhxq2AWnpCArIx+jxo5BW2cIe/dvQTymI7cgB+2dRxCKnQMjEgRJgceZB6dYBNGVjWX3duKy4uPvSoT9DgKb1q2KY5q62dBwHJkRjfgMXlQkxHLd7FxBBmsp8JAukbFfkFq//09sZlQvPdUx5uZ3Dyxw8Z2WIl8jhucd7S70tcYlMR5mEKWE4cyhcGTEjHwcj1wL5io1TcYweAtpzeOCIIE3VLFwHMFABKebWhEI9iArLxPjLi6DwQS8s24bwsF2dHYeRjjegrCqmyb3rTlVKC4twC//89dQHAQFRUMR6GlFS+spDC2YjpKy8Xi0djuuKDvxjsPdPI/MOhpnr1X+LaWYQAnKCEgpQNxgrIcR7IHB9kiexA4ya3vPH0rh/m6/7HjrqJte2TTXqWouOFxOjLtoHLKz+bS1FaIkQ9VzEYi40dymgVLdLDNOWYZhGNA0HZIkQVFkUJ0i1B1BNKxCEiW4PG4oDgWBUARnmlvQGm3Hx3vrwIw2uJVcuBxZcMr5+JdH/xEzv3kxbl/4OPbs3YvrZs1DOBbF2+t/jVnXLMSIkVdjXvkSTMw/9I6Dtc9NZd/hT4NfU7m88dyoG3/70fWOrhCDwIE4HOaH8zLEhwMO0tANiJIEQUxe2AB0TYcvw2t2GXwN35lR4wmz8eDnCwxDMy86apQhQSkyC51wZVFs27YN8R4VQzKHQ4AM6mzBbf9wG15+eR0OHdsDxS2hfNgofHrQj5/+5CEcOOTADSOfwZVDz7ytGO3zBhB81W8PNpff8GL9LCVB3eYlavOiIQdKBCiKE7KiIBaLQ40loKoqEgkeDGoOLpIom4OcKBFomgpFdpnnb/kg0hNuR0vbKSiyE9m5OfjZotvgypTx+spdeK/uINRgCBH1FNpCxzCsZCraOzuhshgiaovZACmKBF9WNiKahCX/0IVrJoTXiu3hGnLnLu3zluX/j+wrV3xyomz+f62dKcc1vhlpbQERgU9kBhJxDbpuy1tR4PF6MbS0EJmZPhi6jmB3GHHVQCLBJzYDsgwo/L9YxDvQcOQAIMTBdAMZngz802O3obJ6FE4fieL5p7bgk71b0RU8AU3XkJs7FqMuysQtf38djhw5jUW/fAmdXc2gAt/CTuD9J8bj0iFn1orZZB6ZucE8ovJFb/1yvvqVA2eGzX92/RyJg+cNgiAQM497b/yyFN8WMqxNAp1fPw8HIYiAx+OBJCmQJBmiIIOIEiCrZlmLqhSNRxrR1XEOVI/B6/Ni6lXV5iGEA/sbcbbtuKkenycPwVg77r6vBhddMgz++k+x9Nk6K20MHYHIUbz5L7mYPjK0VtxH5pFHBgi8UVe1cv/pkTXPrb9e0nXJYp0AikOyNwXNJspUQt/mBB8YCIgoIB7XEI2GEYuH0NndipAawH8/dz9GjS9Gd0DDsiX12FK/G7oeNC9E5mWOhWZ041xnI1TaicKCUXA7stAdPglB1hGJRtATjkNPyMjLK8IVV03Gh/4P8J83NeIbl+prxX3116e63/jHS92aqtV7T5XPffqtb/L5y1xnHk3h2xL9ztUmz9bwCcphGh8/wsLP4Ejm0MKlq+lxBBPnkFkYx48fuxnFJflY8fw+rPrNVjAtDrfswCWXjwPkKDZv3o0DjRtBBAVudyYCkbOIRM4hL7cMFRXVCHbruOf+Wlw3bzx+9/J+jGx5EF+fpA4w+LqqNXtPDK9Z/O5s84uYN8YvE4kQRQ7OuiJrFnPzqq0Al8sJSRLNK9emMQqiuZsSiUXQ0HgIUS2I3GKCCZOGwi2VY7v/ILSohqK8TNz36DXIyndixYs78dKLSzGmYgzuuGcO3n5nHz58dzs02gnBSUHELFw6+Up88/qvoaGhHV+Tf45pY9vXijX1c5LHmr58zq+pfm3/qeFzX3ivFoIkmyCISOBxO+FwKGYz43G74PK4AFGA7JBNwO1tbWhqakbTmXOIxxPmWkVRUJRfYvbzokOA7JPx8ScfINAehEfyIsvrxcI7ZiGvIBeLFv0auz/5CAWFJVj71vP46U/fw96de6CzECJaD9zePDhc2TAkioKScvxk6lpUjm4b8KNob+w/PXzO0vobIMkSNF2HIIlwOFyIqSq0hIZwOIRYLIaErkPTDTgUJ1yKAlEQoMgOuBwuOGXFVIp5ssoQEdOjyCn14tqai1C/YSdef20dYtEw1ES32TMYLAGR/+c7XUFebjF6Ql2gmoisnCJ8+7uz0NbRg1dXv4XckR50jyjAb6oP4zL56ECDr/79zqNDZpunp2xXT27BWieqbA/oPT9LzNNRljfw+7wv4KZolcjkJWdJFuFyOyApArqDPWjvaIXBDLPz4+t4f8D7B5fbCZfbhTNnzkIS+XcQUVRcYHaO7e0BUEGDkUGQEClmFYbflGr8139RuZ831ZmHiCdWv/Fl3+zP93pmDOghxD/fF//r+aQB2cP764GT2jdJg08tXoNndZr5wcNlakjSzKcWr8GzOs384OEyNSRp5lOL1+BZnWZ+8HCZGpI086nFa/CsTjM/eLhMDUma+dTiNXhWp5kfPFymhiTNfGrxGjyr08wPHi5TQ5JmPrV4DZ7VaeYHD5epIUkzn1q8Bs/qNPODh8vUkKSZTy1eg2d1mvnBw2VqSNLMpxavwbM6zfzg4TI1JGnmU4vX4FmdZn7wcJkakjTzqcVr8KxOMz94uEwNSZr51OI1eFanmR88XKaGJM18avEaPKvTzA8eLlNDkmY+tXgNntVp5gcPl6khSTOfWrwGz+o084OHy9SQpJlPLV6DZ/UFzfz/AMyRslA5swLYAAAAAElFTkSuQmCC"
    # 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.

Mais