SecurimageDecaptcha

Solving the Securimage captcha library by audio recognition using convnets via tensorflow

Tip

This project was made for NTHUMods!
Go check it out! It's open source too!

Installation (API)

To run the recognition API

pip install -r requirements.txt

Then run the script

./run.sh

Or run manually

sudo gunicorn main:app --workers 1 --worker-class uvicorn.workers.UvicornWorker --bind 0.0.0.0:5001

Usage (API)

Make a GET request to the api, passing in the url of the audio and (optional) php session id

https://example.api:5001?url=AUDIOURL&id=PHPSESSID

Or visit the fastAPI docs at

https://example.api:5001/docs

Usage (Pre-trained Model)

Two formats of the audio recognition model is available in the /models folder
These models are for single digit recognition, separation is still required

• Tensorflow saved_model format
• TFLite tflite format

Loading the saved_model format

import tensorflow as tf

model = tf.saved_model.load("saved")
# tf operation #1
prediction = model('./path/to/audio')
# tf operation #2
prediction = model(AUDIO_DATA)

Loading the tflite format

import tflite_runtime.interpreter as tflite

model = tflite.Interpreter(model_path='model.tflite')

#or

import tensorflow as tf

model = tf.lite.Interpreter(model_path='model.tflite')


input_details = model.get_input_details()
output_details = model.get_output_details()
model.allocate_tensors()
model.set_tensor(input_details[0]['index'], x)
model.invoke()

prediction = model.get_tensor(output_details[0]['index'])

Usage (Training/Notebook)

The full experimental process and training was done on Google Colab
The notebook can be found in the /notebooks folder
Data used for training can be found in the /training folder

Contributing

Pull requests are always welcomed.

License

MIT