13-06-2012, 04:19 PM
A CAPTCHA Based On Image Orientation
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ABSTRACT
We present a new CAPTCHA which is based on identifying an
image’s upright orientation. This task requires analysis of the
often complex contents of an image, a task which humans usually
perform well and machines generally do not. Given a large
repository of images, such as those from a web search result, we
use a suite of automated orientation detectors to prune those
images that can be automatically set upright easily. We then
apply a social feedback mechanism to verify that the remaining
images have a human-recognizable upright orientation. The main
advantages of our CAPTCHA technique over the traditional text
recognition techniques are that it is language-independent, does
not require text-entry (e.g. for a mobile device), and employs
another domain for CAPTCHA generation beyond character
obfuscation. This CAPTCHA lends itself to rapid implementation
and has an almost limitless supply of images. We conducted
extensive experiments to measure the viability of this technique.
INTRODUCTION
With an increasing number of free services on the internet, we
find a pronounced need to protect these services from abuse.
Automated programs (often referred to as bots) have been
designed to attack a variety of services. For example, attacks are
common on free email providers to acquire accounts. Nefarious
bots use these accounts to send spam emails, to post spam and
advertisements on discussion boards, and to skew results of online
polls.
To thwart automated attacks, services often ask users to solve a
puzzle before being given access to a service. These puzzles, first
introduced by von Ahn et al. in 2003[2], were CAPTCHAs:
BACKGROUND: CAPTCHAs
Traditional CAPTCHAs require the user to identify a series of
letters that may be warped or obscured by distracting backgrounds
and other noise in the image. Various amounts of warping and
distractions can be used; examples are shown in Figure 2.
Recently, many character recognition CAPTCHAs have been
deciphered using automated computer vision techniques. These
methods have been custom designed to remove noise and to
segment the images to make the characters amenable for opticalcharacter
recognition [3][4][5].
DETECTING ORIENTATION
The interest in automated orientation detection rapidly arose with
the advent of digital cameras and camera phones that did not have
built-in physical orientation sensors. When images were taken,
software systems needed a method to determine whether the
image was portrait (upright) or landscape (horizontal). The
problem is still relevant because of the large scale scanning and
digitization of printed material.
The seemingly simple task of making an image upright is quite
difficult to automate over a wide variety of photographic content.
There are several classes of images which can be successfully
oriented by computers. Some objects, such as faces, cars,
pedestrians, sky, grass etc. [22][23], are easily recognizable by
computers. It is important to note that computer-vision techniques
have not yet been successful at unconstrained object detection;
therefore, it is infeasible to recognize the vast majority of objects
in typical images and use the knowledge of the object’s shape to
orient the image.
USER EXPERIMENTS
In this section, we describe two user studies. The first study was
designed to determine whether this system would result in a viable
CAPTCHA system in terms of user-success rates and bot-failure
rates. The second study was designed to informally gauge user
reactions to the system in comparison to existing CAPTCHAs.
Since these were uncontrolled studies, we did not measure taskcompletion
times.