The need to tell human and machines apart has surged due to abuse of automated ?bots?. However, several textualimage- based CAPTCHAs have been defeated recently, calling for the development of new anti-automation schemes. In this paper, we propose a simple yet effective visual CAPTCHA test by exchanging the content of nonoverlapping regions in an image. We give in-depth analysis regarding the choice of parameter and image database during the test generation phase. We also contemplate possible ways, including 1) random guess, 2) collect and match, and 3) image segmentation, to defeat the proposed test and provide counter-measures when necessary. Preliminary experimental results have validated the efficacy of the proposed CAPTCHA, although we expect that a large-scale experiment to collect and analyze user responses will contribute to optimal parameter settings.