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Deep-reCaptcha

recaptcha

Deep-reCaptcha is a recaptcha solver built using python and selenium. It uses deep learning to predict the captcha in action and click it. Deep-reCaptcha uses a pretrained model, efficientNet v2, the M variant used for the balance between size and accurecy. This project is made for educational purposes.

Installation and Usage

python 3.9 is used. It should work with other versions though. Packages installed are tensorflow 2.10, numpy, selenium and pillow. The script will check for the model first and download it if not present from github, then will launch the reCaptcha demo page and solve it. the 4x4 captcha type is skipped for now as I have not implemented

As for selenium, I am using firefox. download the last version of gecko driver

about the model

The model was trained on a combination of datasets but mainly on brian-the-dev recaptcha dataset found on github. A model built up by first fitting the model without any training paramaters and then fine tune it multiple time to achieve the best accurecy with minimal val loss.

Classes in the model are:

CLASSES  = [
    "bicycle",
    "bridge",
    "bus",
    "car",
    "chimney",
    "crosswalk",
    "hydrant",
    "motorcycle",
    "other",
    "palm",
    "stairs",
    "traffic_light"
]

an ImageDataGenerator used to increase the training size with augementations

train_datagen = ImageDataGenerator( preprocessing_function=preprocess_input ,
                                   validation_split=0.2,
                                   rotation_range = 30,
                                   width_shift_range = 0.2,
                                   height_shift_range = 0.2,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True,
                                   )

test_datagen = ImageDataGenerator( preprocessing_function=preprocess_input,
                                  validation_split=0.2,
                                  )

A baseline fit with no trainable params was excuted with around 20 epochs with early stop to avoid overfitting. after that a loop of training was made with incrementel increase in trainable params.

#finetuning

n_epochs = 5
batch_size = 50
nb_finetune_samples = nb_train_samples

reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(
    monitor="val_loss",
    factor=0.1,
    patience=1,
    verbose=0,
    mode="auto",
    min_delta=0.00001,
    cooldown=0,
    min_lr=0,
)

early_stop = tf.keras.callbacks.EarlyStopping(
    monitor="val_loss",
    patience=10,
    verbose=0,
    mode="auto",
    restore_best_weights=True,

)

checkpoint = tf.keras.callbacks.ModelCheckpoint('checkpoint.h5',monitor='val_loss', save_best_only=True)


percentage_to_not_train = 100

for _ in range(7):
    
    percentage_to_not_train = percentage_to_not_train - 10
    
    ltt = len(base_model.layers) // percentage_to_not_train


    base_model.trainable = True
    for layer in base_model.layers[:int(ltt)]:
        base_model.trainable = False

    model.compile(optimizer=tf.keras.optimizers.Adam(0.00001),
     loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
     metrics=["accuracy"],
     )
    
    training = model.fit(train_generator,
    steps_per_epoch=nb_finetune_samples//batch_size,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples//batch_size,
    epochs=n_epochs,
    callbacks=[reduce_lr, checkpoint, early_stop])

At the end, an accurecy of 74% was reached which is more than enough for a captcha in my opinion (those can be daunting even for humans).

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.