masasron / DeCAPTCHA

This repository demonstrates how LLaVA, on a MacBook Pro, can solve Google reCAPTCHA visual challenges, even with added noise and occlusion.

DeCAPTCHA

This repository showcases a proof of concept where LLaVA, running on a MacBook Pro, effectively solves Google reCAPTCHA visual challenges, even with added noise and occlusion. This achievement marks a significant shift in the landscape of traditional visual CAPTCHA challenges, attributable to the emergence of LLMs and multimodal technologies.

> python decaptcha.py --target "bus" --preview 1 ./examples/noisy-bus.jpg
[INFO] Square 0:  0
[INFO] Square 1:  1
[INFO] Found match in square 1
[INFO] Square 2:  0
[INFO] Square 3:  0
[INFO] Square 4:  0
[INFO] Square 5:  0
[INFO] Square 6:  0
[INFO] Square 7:  1
[INFO] Found match in square 7
[INFO] Square 8:  1
[INFO] Found match in square 8
1,7,8

⚠️ It is crucial to understand that this approach does not represent a comprehensive solution for bypassing Google's reCAPTCHA. Google employs a multifaceted system that considers various factors, including users' interactions and behavior patterns before and during the reCAPTCHA challenges, to determine if the user is a bot.

Prerequisites

• Python 3.6 or higher.
• Clone llama.cpp from here and follow the instructions to build the project.
• Download one of the LLaVA v1.5 7b models from here along with the multimodal projector file mmproj-model-f16.gguf.
• Serve the model locally using the server executable in the llama.cpp repository as follows: ./server -m ./ggml-model-f16.gguf --mmproj ./mmproj-model-f16.gguf.

Installation

• Clone this repository.
• Install the requirements using pip install -r requirements.txt.

Usage

• Download a reCAPTCHA image challenge from here.
• Run the script with python decaptcha.py --target "bus" --preview 1 ./examples/noisy-bus.jpg.

usage: decaptcha.py [-h] --target TARGET [--preview PREVIEW] [--blur_radius BLUR_RADIUS] [--server_url SERVER_URL] [--log_level {DEBUG,INFO,WARNING,ERROR,CRITICAL}] [--prompt PROMPT] image_path

DeCAPTCHA

positional arguments:
  image_path            path to the image file

optional arguments:
  -h, --help            show this help message and exit
  --target TARGET       classification target
  --preview PREVIEW     show preview of the solution
  --blur_radius BLUR_RADIUS
                        radius for Gaussian blur
  --server_url SERVER_URL
                        llava server url
  --log_level {DEBUG,INFO,WARNING,ERROR,CRITICAL}
                        logging level
  --prompt PROMPT       classification prompt for llava

Disclaimer

This is a proof of concept and is not intended for malicious use. Please act responsibly.