  To efficiently monitor the growth and evolution of a particular wildlife
population, one of the main fundamental challenges to address in animal ecology
is the re-identification of individuals that have been previously encountered
but also the discrimination between known and unknown individuals (the
so-called "open-set problem"), which is the first step to realize before
re-identification. In particular, in this work, we are interested in the
discrimination within digital photos of beluga whales, which are known to be
among the most challenging marine species to discriminate due to their lack of
distinctive features. To tackle this problem, we propose a novel approach based
on the use of Membership Inference Attacks (MIAs), which are normally used to
assess the privacy risks associated with releasing a particular machine
learning model. More precisely, we demonstrate that the problem of
discriminating between known and unknown individuals can be solved efficiently
using state-of-the-art approaches for MIAs. Extensive experiments on three
benchmark datasets related to whales, two different neural network
architectures, and three MIA clearly demonstrate the performance of the
approach. In addition, we have also designed a novel MIA strategy that we
coined as ensemble MIA, which combines the outputs of different MIAs to
increase the attack accuracy while diminishing the false positive rate.
Overall, one of our main objectives is also to show that the research on
privacy attacks can also be leveraged "for good" by helping to address
practical challenges encountered in animal ecology.
