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Machine Learning Attacks Against the ASIRRA CAPTCHA
Authors: Philippe GolleAbstract:
The ASIRRA CAPTCHA [EDHS2007], recently proposed at ACM CCS 2007, relies on the problem of distinguishing images of cats and dogs (a task that humans are very good at). The security of ASIRRA is based on the presumed difficulty of classifying these images automatically. In this paper, we describe a classifier which is 82.7% accurate in telling apart the images of cats and dogs used in ASIRRA. This classifier is a combination of support-vector machine classifiers trained on color and texture features extracted from images. Our classifier allows us to solve a 12-image ASIRRA challenge automatically with probability 10.3%. This probability of success is significantly higher than the estimate given in [EDHS2007] for machine vision attacks. The weakness we expose in the current implementation of ASIRRA does not mean that ASIRRA cannot be deployed securely. With appropriate safeguards, we believe that ASIRRA offers an appealing balance between usability and security. One contribution of this work is to inform the choice of safeguard parameters in ASIRRA deployments.
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