hcaptcha-challenger 0.0.1.1

🥂 Gracefully face hCaptcha challenge with Yolov5(ONNX) embedded solution.

hCaptcha Challenger

馃殌 Gracefully face hCaptcha challenge with Yolov5(ONNX) embedded solution.

Introduction

Does not rely on any Tampermonkey script.

Does not use any third-party anti-captcha services.

Just implement some interfaces to make AI vs AI possible.

Requirements

• Python 3.7+
• google-chrome

Usage

1. Clone the project code in the way you like.

2. Execute the following command in the project root directory.

   # hcaptcha-challenger
   pip install -r ./requirements.txt
   

3. Download Project Dependencies.

   The implementation includes downloading the YOLOv5 object detection model and detecting google-chrome in the current environment.

   If google-chrome is missing please follow the prompts to download the latest version of the client, if google-chrome is present you need to make sure it is up to date.

   Now you need to execute the cd command to access the src/ directory of project and execute the following command to download the project dependencies.

   # hcaptcha-challenger/src
   python main.py install
   

4. Start the test program.

   Check if chromedriver is compatible with google-chrome.

   # hcaptcha-challenger/src
   python main.py test
   

5. Start the demo program.

   If the previous test passed perfectly, now is the perfect time to run the demo!

   # hcaptcha-challenger/src
   python main.py demo
   

Advanced

1. You can download yolov5 onnx models of different sizes by specifying the model parameter in the install command.

   • Download yolov5s6 by default when no parameters are specified.

   • The models that can be chosen are yolov5n6锛宍yolov5m6锛宍yolov5s6.

   # hcaptcha-challenger/src
   python main.py install --model=yolov5n6
   

2. You can run different yolo models by specifying the model parameter to compare the performance difference between them.

   • Similarly, when the model parameter is not specified, the yolov5s6 model is used by default.

   • Note that you should use install to download the missing models before running the demo.

   # hcaptcha-challenger/src
   python main.py demo --model=yolov5n6
   

3. Specify the challenge source.

   Mapping the target site's hcaptcha iframe to the demo site by specifying the site-key. The selectable sources can be viewed in the variable _SITE_KEYS.

   # hcaptcha-challenger/src
   python main.py demo --target=discord
   

4. Specify the challenge language.

   [dev] Start the challenge in the specified language and the page elements will be replaced. Currently only zh and en are supported.

   # hcaptcha-challenger/src
   python main.py demo --lang=en
   

Solutions

You may be surprised by the lack of a pass rate in the following table, but if you have run the hcaptcha-challenger demo, you will see that it is almost impossible to fail a challenge, i.e. the pass rates for the various solutions provided by hcaptcha-challenger are almost close to THE ONE.

YOLOv5(onnx)

The following table shows the average solving time of the hCAPTCHA challenge for 30 rounds (one round for every 9 challenge images) of mixed categories processed by onnx models of different sizes.

• Use of the YOLOv5n6(onnx) embedded scheme to obtain solution speeds close to the limit.
• Use of the YOLOv5s6(onnx) embedded solution, which allows for an optimal balance between stability, power consumption, and solution efficiency.

model(onnx)	avg_time(s)	size(MB)
yolov5n6	0.71	12.4
yolov5s6	1.422	48.2
yolov5m6	3.05	136

SK-IMAGE

The following table shows the speed statistics of solving for specific labels using image segmentation in the same experimental setting.

• The solving time is negligible using the rainbow method, which is as fast as taking a dictionary value.
• The advantage of the base method is that it does not rely on any external model and can perform tasks in a variety of low-configuration containers through image processing alone.

method(SK-IMAGE)	avg_time(ms)	size(MB)
vertical river (base)	2883	/
vertical river (rainbow)	/	0.29
airplane in the sky flying left (base)	30	/
airplane in the sky flying left (rainbow)	/	0.29

Tour

Install Google Chrome on Ubuntu 18.04+

1. Downloading Google Chrome

   wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
   

2. Installing Google Chrome

   sudo apt install ./google-chrome-stable_current_amd64.deb
   

Install Google Chrome on CentOS 7/8

1. Start by opening your terminal and downloading the latest Google Chrome .rpm package with the following wget command :

   wget https://dl.google.com/linux/direct/google-chrome-stable_current_x86_64.rpm
   

2. Once the file is downloaded, install Google Chrome on your CentOS 7 system by typing:

   sudo yum localinstall google-chrome-stable_current_x86_64.rpm
   

Install Google Chrome on Windows / MacOS

Just go to Google Chrome official website to download and install.

Reference

• hCaptcha challenge template site @maximedrn
• ultralytics/yolov5: YOLOv5 馃殌
• ultrafunkamsterdam/undetected-chromedriver