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Captcha bypass tutorials

How to bypass captchas using AI captcha solver

How to bypass captchas using AI

This guide explains technical methods for bypassing CAPTCHAs with AI, compares solver types, and highlights production-grade services like SolveCaptcha and 2Captcha.

AI CAPTCHA solvers automate solving of visual, textual, and interactive CAPTCHA challenges using:

  • CNN-based image classifiers
  • OCR pipelines
  • Heuristic or synthetic behavior engines
  • Multimodal reasoning (vision + LLMs)

They target systems designed to block scripted and automated traffic.

Core AI techniques

1. Image classification

Used to solve:

  • Grid-based selection tasks
  • Slider CAPTCHAs (template matching, mask prediction)

Frameworks: PyTorch, TensorFlow, ONNX

2. OCR (Optical Character Recognition)

Used for:

  • Alphanumeric distorted text CAPTCHAs

Toolchains: Tesseract, CRNN, ResNet + LSTM

3. Synthetic behavior

Mimics:

  • Mouse paths
  • Click delays
  • Input timing and hesitation

Useful for gesture-based CAPTCHAs and fingerprint-sensitive systems.

4. Language-driven reasoning

In early use for logic-based CAPTCHAs.

  • Vision-Language Models
  • Chain-of-Thought prompting for multi-step puzzles

Solvable captcha types

Type Solved by AI Requires Human
Image grid selection ✅ ⛔️
Text distortion ✅ ⛔️
Slider puzzle ⚠️ Partial ✅
Interaction tracking ❌ ✅
Logic-based puzzles ⚠️ Partial ✅

Solver options

Self-hosted (research-grade)

Pros:

  • Full control
  • No 3rd-party calls

Cons:

  • Requires datasets
  • GPU inference infra
  • CAPTCHA format drift breaks models

External services (production-grade)

SolveCaptcha

  • Supports token-based CAPTCHA solving via API
  • Fast resolution with proxy support
  • Integrates with browser automation (Puppeteer, Selenium)
  • Offers SDKs in Python, PHP, JS, and more
  • Uses POST JSON payload with keys like sitekey, pageurl, and optional proxy
  • Returns structured JSON with code or error explanation
  • Supports token injection workflows (e.g. JS g-recaptcha-response)

GitHub: SolveCaptcha

2Captcha

  • Hybrid AI + Human system
  • Human fallback for puzzles AI can’t solve
  • Reliable for edge cases (behavioral, interactive)
  • Compatible with all CAPTCHA types

GitHub: 2Captcha

Limitations of AI captcha Solvers

  • Format variability: Frequent changes to CAPTCHA structure break hardcoded AI models
  • Behavioral detection: AI struggles to simulate genuine user behavior patterns
  • Model degradation: Accuracy drops without continuous retraining on new samples
  • Context binding: Some CAPTCHAs validate interaction context (DOM state, cursor history). AI solvers without browser context will fail unless used with full automation stack (e.g. Puppeteer + token injection).
  • Fingerprinting resistance: CAPTCHA providers track canvas entropy, WebGL output, audio stack, and more. AI solving is often just one layer — real bypass may require full browser fingerprint spoofing.
  • Fallback required: When AI fails, services like 2Captcha resolve via manual input

Summary

AI CAPTCHA solvers can efficiently bypass most static and image-based CAPTCHAs. But for real-world applications—scraping, browser automation, penetration testing—using hybrid solvers like SolveCaptcha with human fallback from 2Captcha offers higher success rates and resilience against evolving protection mechanisms.