International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.3, May 2018
2. BACKGROUND
Fig. 2. CAPTCHA datasets
CAPTCHAs can be found almost in every websites [24] [14] [18]. Each type of CAPTCHA should be solved accordingly because of the unique encoding algorithms. Correspondingly, a lot of deCAPTCHA algorithms have been proposed for most famous companies such as Google, Microsoft, and Facebook. A low-cost attack on a Microsoft CAPTCHA was presented for solving the segmentation resistant task, which achieved a segmentation success rate of higher than 90% [27]. Moreover, another projection-based segmentation algorithm for breaking MSN and YAHOO CAPTCHAs proves to be effective, which doubled the corrected segmentation rate over the traditional method [6]. Besides, an algorithm using ellipse-shaped blobs detection for breaking Facebook CAPTCHA also presents to be useful [15]. As can be seen, Fig. 1 presents some examples of text-based CAPTCHAs which will be discussed later. Fig. 2 describes the system of our datasets. Fig.3 shows the basic flow chart to defeat the CAPTCHAs. In this section, we will demonstrate the whole process.
Fig. 3. Basic Flow Chart to defeat the CAPTCHA
2.1 Prepossessing Step There are several possible techniques to denoise the images based on various types of each other. After our efforts, we find out the appropriate ways according to the different types of examples. 21