Oh...good point my friend. I had just read something about that very thought. Here's a bit to chew on. You are hungry aren't you? Hehe...:
In October 2013, artificial intelligence company Vicarious claimed that it had developed software that was able to solve modern CAPTCHAs with character recognition rates of up to 90%.[15] Unlike the previous one-off successes that made use of flaws in specific CAPTCHA tests, Vicarious asserted that its algorithms were powered by a holistic vision system modeled after insights from the human brain. The company also indicated that its AI was not specifically designed to complete CAPTCHA but rather to correctly recognize photographs, videos, and other visual data. However, Luis von Ahn, a pioneer of early CAPTCHA and founder of reCAPTCHA, expressed skepticism, stating: "It's hard for me to be impressed since I see these every few months." He pointed out that 50 similar claims to that of Vicarious's have been made since 2003.
Computer character recognition
Although CAPTCHAs were originally designed to defeat standard OCR software designed for document scanning, a number of research projects have proven that it is possible to defeat many CAPTCHAs with programs that are specifically tuned for a particular type of CAPTCHA. For CAPTCHAs with distorted letters, the approach typically consists of the following steps:
Removal of background clutter, for example with color filters and detection of thin lines.
Segmentation, i.e. splitting the image into segments containing a single letter.
Identifying the letter for each segment.
Step 1 is typically very easy to do automatically. In 2005, it was also shown that neural network algorithms have a lower error rate than humans in step 3.[22] The only part where humans still outperform computers is step 2. If the background clutter consists of shapes similar to letter shapes, and the letters are connected by this clutter, the segmentation becomes nearly impossible with current software. Hence, an effective CAPTCHA should focus on step 2, the segmentation.
Neural networks have been used with great success to defeat CAPTCHAs as they are generally indifferent to both affine and non-linear transformations. As they learn by example rather than through explicit coding, with appropriate tools very limited technical knowledge is required to defeat more complex CAPTCHAs.
Some CAPTCHA-defeating projects:
Mori et al. published a paper in IEEE CVPR'03 detailing a method for defeating one of the most popular CAPTCHAs, EZ-Gimpy, which was tested as being 92% accurate in defeating it.[23] The same method was also shown to defeat the more complex and less-widely deployed Gimpy program 33% of the time. However, the existence of implementations of their algorithm in actual use is indeterminate at this time.
PWNtcha has made significant progress in defeating commonly used CAPTCHAs, which has contributed to a general migration towards more sophisticated CAPTCHAs.[24]
A number of Microsoft Research papers describe how computer programs and humans cope with varying degrees of distortion.[22]
