2017-08-08 22:06:59 +02:00
2017-08-08 14:43:20 +02:00
2017-08-08 21:46:47 +02:00
2017-08-08 14:43:20 +02:00
2017-08-08 14:40:54 +02:00
2017-08-08 14:43:20 +02:00
2017-08-08 21:46:47 +02:00
2017-08-08 21:46:47 +02:00
2017-08-08 22:06:59 +02:00
2017-08-08 14:43:20 +02:00
2017-08-08 14:43:20 +02:00
2017-08-08 21:46:47 +02:00

goCaptcha

captcha server, with own datasets, to train own machine learning AI

How to use?

  1. Get the captcha:
GET/ 127.0.0.1:3025/captcha

Server response:

{
    "id": "881c6083-0643-4d1c-9987-f8cc5bb9d5b1",
    "imgs": [
        "7cf6f630-e78f-469c-85dd-2d677996fea1.png",
        "d4014318-f875-4b42-b704-4f5bf5e5e00c.png",
        "2dd69b44-903d-4e78-bb7b-f8b07877c9e5.png",
        "2954fc38-819d-40c9-ae3e-7b6fbb68ddbe.png",
        "b060f58a-d44b-4e05-b466-92aa801a2aa1.png",
        "1b838c46-b784-471e-b143-48be058c39a7.png"
    ],
    "question": "leopard",
    "date": ""
}
  1. User selects the images that fit in the 'question' parameter (in this case, 'leopard')

  2. Post the answer. The answer contains the CaptchaId, and an array with the selected images

POST/ 127.0.0.1:3025/answer

Post example:

{
	"captchaid": "881c6083-0643-4d1c-9987-f8cc5bb9d5b1",
	"selection": [0,0,0,0,1,1]
}

Server response:

true

How this works?

Server reads dataset

First, server reads all dataset. Dataset is a directory with subdirectories, where each subdirectory contains images of one element.

For example:

imgs/
    leopard/
        img01.png
        img02.png
        img03.png
        ...
    laptop/
        img01.png
        img02.png
        ...
    house/
        img01.png
        img02.png
        ...

Then, stores all the filenames corresponding to each subdirectory. So, we have each image and to which element category is (the name of subdirectory).

Server generates captcha

When server recieves a GET /captcha, generates a captcha, getting random images from the dataset.

For each captcha generated, generates two mongodb models:

Captcha Model
{
    "id" : "881c6083-0643-4d1c-9987-f8cc5bb9d5b1",
    "imgs" : [
        "7cf6f630-e78f-469c-85dd-2d677996fea1.png",
        "d4014318-f875-4b42-b704-4f5bf5e5e00c.png",
        "2dd69b44-903d-4e78-bb7b-f8b07877c9e5.png",
        "2954fc38-819d-40c9-ae3e-7b6fbb68ddbe.png",
        "b060f58a-d44b-4e05-b466-92aa801a2aa1.png",
        "1b838c46-b784-471e-b143-48be058c39a7.png"
    ],
    "question" : "leopard"
}
CaptchaSolution Model
{
    "id" : "881c6083-0643-4d1c-9987-f8cc5bb9d5b1",
    "imgs" : [
        "image_0022.jpg",
        "image_0006.jpg",
        "image_0050.jpg",
        "image_0028.jpg",
        "image_0119.jpg",
        "image_0092.jpg"
    ],
    "imgssolution" : [
        "camera",
        "camera",
        "laptop",
        "crocodile",
        "leopard",
        "leopard"
    ],
    "question" : "leopard"
}

Both models are stored in the MongoDB.

Captcha Model contains the captcha that server returns to the petition. And CaptchaSolution contains the solution of the captcha. Both have the same Id.

Server validates captcha

When server recieves POST /answer, gets the answer, search for the CaptchaSolution based on the CaptchaId in the MongoDB, and then compares the answer 'selection' parameter with the CaptchaSolution.

If the selection is correct, returns 'true', if the selection is not correct, returns 'false'.

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