白鲸识别的成员推断攻击
Membership Inference Attack for Beluga Whales Discrimination
摘要：
为了有效地监测特定野生动物种群的生长和进化，动物生态学中要解决的主要基本挑战之一是重新识别先前遇到的个体，以及区分已知和未知个体（所谓的“开集问题”），这是重新识别之前要实现的第一步。特别是，在这项工作中，我们感兴趣的是白鲸的数码照片中的歧视，这是已知的最具挑战性的海洋物种之一，以区别由于他们缺乏独特的功能。为了解决这个问题，我们提出了一种基于成员推断攻击（MIA）的新方法，MIA通常用于评估与发布特定机器学习模型相关的隐私风险。更确切地说，我们证明了已知和未知的个人之间的区别的问题，可以有效地解决使用国家的最先进的方法MIA。在与鲸鱼相关的三个基准数据集、两种不同的神经网络架构和三种MIA上进行的大量实验清楚地证明了该方法的性能。此外，我们还设计了一种新的MIA策略，我们创造了集成MIA，它结合了不同的MIA的输出，以提高攻击的准确性，同时降低误报率。总的来说，我们的主要目标之一也是表明，对隐私攻击的研究也可以通过帮助解决动物生态学中遇到的实际挑战来“永远”利用。
英文摘要：
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.
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