明慧--Introduction (cont.)

In [2], Ahn et al. proposed a method: CAPTCHA, which stands for “Completely Automated Public Turing test to Tell Computers and Human Apart”, to separate human user from bots. It later became probably the most common method of limiting access to services made available over the Web. Most CAPTCHAs are in the form of visual verification of a bitmapped image, although alternative solutions such as audio and logic puzzles have also been exploited.

Ahn et al.提出一個方法：CAPTCHA，用來分辨使用者和機器人程式，後來變成最常使用的方法，常用來限制網路上一些經常使用的服務。而大部分的CAPTCHA是在二為影像中利用視覺來做確認，現在已經有一些聲音及拼圖的辨識也被使用。


The incorporation of visual CAPTCHA mechanism with textual images is now frequently seen in the comment areas of message boards and personal blogs due to the low cost in generating such a test. However, it is also relatively easy to defeat textual-image-based CAPTCHA. In [3], Mori and Malik developed a robust character recognition algorithm using shape context and achieved 93% accuracy on a set of images generated by EZ-Gimpy. A more difficult test named ‘Gimpy-r’ has also been broken [4]. It is only a matter of time that bots with these functionalities be widespread. Consequently, more effective solutions must be sought.

(這段我看不太懂...)現在比較常見的視覺CAPTCHA是用在留言板及個人的部落格，他就像是個測驗，但也很容易將以貼圖影像為基礎的CAPTCHA破解。在“Recognizing Objects in Adversarial Clutter: Breaking a Visual CAPTCHA”這篇論文中，Mori和Malik發明一種特徵辨識的演算法，利用形狀互換及藉由EZ-Gimpy一堆的影像生成達到93％的正確率，而更難的Gimpy-r測驗也被破解了。機器人程式的功能自然而然的已經被廣泛流傳，而更有效的方法也不斷在發掘當中。


The textual-image-based approach reported in [5] marks our first effort to address the problem raised above. Instead of using distorted characters with random backgrounds, we filled both foreground and background layers with random textures. An example is shown in Fig. 1.

在“Embedding Information within Dynamic Visual Patterns”這篇論文中指出我們第一個以貼圖影像為基礎的作品，已經將上面的問題解決了，不是利用背景的特徵作扭曲，而是在前景及背景兩部分都做隨機貼圖。