Internet services are increasingly abused by malicious scripts that
try to mimic human users. Reverse Turing tests are challenges used
to differentiate humans from computers. Visual reverse Turing tests
use visual challenges, such as distorted character recognition
tasks, that are easily solved by humans, while remaining too hard
for automatic scripts. In this project, we demonstrate that the
computational and development cost of a script breaking through
some currently deployed visual reverse Turing tests is low, thus
making them ineffective in protecting these services. We several
case studies of successful attacks on character-based tests that
are currently used to protect public web services. Our attacks
utilize image processing techniques and also exploit flaws in the
test deployment.
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