Web Scraping & Bots · 강의

CAPTCHA 해결 전략

수동 해결, 타사 서비스 및 머신 러닝 접근법을 포함해 CAPTCHA를 처리하는 방법을 살펴봅니다.

CAPTCHA 해결 전략은(는) CoddyKit의 무료 Web Scraping & Bots 강의입니다. 이것은 4개 중 3번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 Web Scraping & Bots 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. Web Scraping & Bots 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

What are CAPTCHAs?

CAPTCHA stands for "Completely Automated Public Turing test to tell Computers and Humans Apart."

They are security measures designed to distinguish between human users and automated bots.

For web scrapers, CAPTCHAs are a common hurdle that prevents automated data extraction.

Why Websites Use Them

Websites use CAPTCHAs to protect against various automated attacks, such as:

Spam and abuse
Credential stuffing
Data scraping
Denial-of-service attacks

They act as a gatekeeper, ensuring only humans can proceed.

Common CAPTCHA Types

You've likely seen different kinds of CAPTCHAs:

Text-based: Distorted letters or numbers to type.
Image recognition: "Select all squares with traffic lights."
Logic puzzles: Simple math or word problems.
Invisible reCAPTCHA: Works in the background, sometimes showing a challenge.

The Bot's Dilemma

For a bot, solving a CAPTCHA is incredibly difficult without specific programming.

Bots struggle with:

Interpreting distorted text
Identifying objects in images
Understanding context or logic

This is by design!

Manual CAPTCHA Solving

The simplest, though not scalable, method is manual solving.

When your bot encounters a CAPTCHA, it pauses, displays the CAPTCHA image to a human, and waits for them to input the solution.

This is only practical for very low-volume, personal scraping tasks.

Third-Party Solving Services

For higher volumes, you can use third-party CAPTCHA solving services.

These services employ human workers (or sometimes AI) to solve CAPTCHAs for you, typically for a small fee per solution.

Examples include 2Captcha, Anti-Captcha, and DeathByCaptcha.

How These Services Work

The process usually involves these steps:

Your bot extracts the CAPTCHA image/data.
It sends this data to the service's API.
The service's workers solve it.
The service sends the solution back to your bot.
Your bot submits the solution to the website.

This integrates seamlessly into your scraping workflow.

Integrating a Solving Service

Here's a conceptual Python example of how you might interact with a CAPTCHA solving service. We simulate sending an image and receiving a solution.

In a real scenario, you'd use an API client for the service.

import requests # For making HTTP requests
import json     # For handling JSON data

# This function simulates sending a CAPTCHA image
# to a third-party service and getting a solution.
def get_captcha_solution(image_data_base64):
    print("Simulating sending CAPTCHA to service...")
    # In reality, you'd replace this with an actual API call.
    # For example:
    # api_url = "https://api.captchasolver.com/solve"
    # payload = {"apiKey": "YOUR_API_KEY", "body": image_data_base64}
    # response = requests.post(api_url, json=payload)
    # return response.json().get("solution", None)

    # For this example, we'll return a dummy solution
    # after a 'processing' message.
    print("Service processing CAPTCHA...")
    return "example_captcha_answer"

if __name__ == "__main__":
    # Imagine you extracted this base64 encoded image from a webpage
    dummy_captcha_image = "iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAA" 
    
    print("Attempting to get CAPTCHA solution...")
    solution = get_captcha_solution(dummy_captcha_image)
    
    if solution:
        print(f"Received solution: '{solution}'")
        print("Now, your bot would submit this solution to the website.")
    else:
        print("Failed to get CAPTCHA solution.")

Machine Learning for CAPTCHAs

Advanced bots sometimes use Machine Learning (ML) to attempt solving CAPTCHAs.

This involves:

OCR (Optical Character Recognition): For text-based CAPTCHAs.
Image Recognition: For identifying objects in image-based CAPTCHAs.

However, this requires significant development and training data.

ML Limitations & Ethics

ML models for CAPTCHAs are complex and often require constant updates as CAPTCHA designs evolve.

Also, bypassing CAPTCHAs, especially reCAPTCHAs, can violate a website's Terms of Service.

Always consider the ethical and legal implications of your scraping activities.

Check Your Understanding

CAPTCHAs are designed to be difficult for bots. Which of these are common strategies employed by websites using CAPTCHAs?

Recap: CAPTCHA Strategies

We've explored how CAPTCHAs protect websites and various strategies to handle them:

Manual solving: Simple, low volume.
Third-party services: Scalable, human-powered.
Machine Learning: Complex, requires development.

Always remember the ethical considerations when bypassing these measures.

자주 묻는 질문

“CAPTCHA 해결 전략” 강의는 무료인가요?

네 — “CAPTCHA 해결 전략” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 Web Scraping & Bots 강의 전체를 잠금 해제할 수 있습니다. Web Scraping & Bots 강의에는 총 4개의 강의가 포함되어 있습니다.

“CAPTCHA 해결 전략”에서 뭘 배우나요?

수동 해결, 타사 서비스 및 머신 러닝 접근법을 포함해 CAPTCHA를 처리하는 방법을 살펴봅니다. 브라우저에서 직접 실행하는 실습 코드로 Web Scraping & Bots을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.

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대부분의 CoddyKit 강의는 약 5~10분이 소요됩니다. 각 강의는 간결하고 인터랙티브하여 꾸준한 진행이 가능하며, 웹과 앱에서 중단한 부분부터 바로 시작할 수 있습니다.

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