“Machine Learning Attacks against the Asirra CAPTCHA”, Phillipe Golle2008-10-01 (AI)⁠:

The Asirra CAPTCHA [EDHS2007], 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 statistically-significantly higher than the estimate of 0.2% given in [EDHS2007_17ya] for machine vision attacks. Our results suggest caution against deploying Asirra without safeguards.

We also investigate the impact of our attacks on the partial credit and token bucket algorithms proposed in [EDHS2007_17ya]. The partial credit algorithm weakens Asirra considerably and we recommend against its use. The token bucket algorithm helps mitigate the impact of our attacks and allows Asirra to be deployed in a way that maintains an appealing balance between usability and security. One contribution of our work is to inform the choice of safeguard parameters in Asirra deployments.

[Keywords: CAPTCHA, reverse Turing test, machine learning, support vector machine, classifier.]

…Our classifier is a combination of 2 support-vector machine⁵ (SVM) classifiers trained on color and texture features of images. The classifier is entirely automatic, and requires no manual input other than the one-time labelling of training images. Using 15,760 color features, and 5,000 texture features per image, our classifier is 82.7% accurate. The classifier was trained on a commodity PC, using 13,000 labeled images of cats and dogs downloaded from the Asirra website¹.