论文标题
在半监督学习的背景下重新思考后门数据中毒攻击
Rethinking Backdoor Data Poisoning Attacks in the Context of Semi-Supervised Learning
论文作者
论文摘要
半监督的学习方法可以训练高准确的机器学习模型，其中一小部分传统监督学习所需的标记培训样本。这种方法通常不涉及对未标记的培训样本进行仔细审查，从而使它们诱人的数据中毒攻击目标。在本文中，我们研究了半监督学习方法的脆弱性，即对未标记样本的后门数据中毒攻击。我们表明，影响中毒样品预测标签分布的简单中毒攻击非常有效 - 达到平均攻击成功率高达96.9％。我们引入了针对半监督学习方法的广义攻击框架，以更好地理解和利用其局限性并激发未来的防御策略。
Semi-supervised learning methods can train high-accuracy machine learning models with a fraction of the labeled training samples required for traditional supervised learning. Such methods do not typically involve close review of the unlabeled training samples, making them tempting targets for data poisoning attacks. In this paper we investigate the vulnerabilities of semi-supervised learning methods to backdoor data poisoning attacks on the unlabeled samples. We show that simple poisoning attacks that influence the distribution of the poisoned samples' predicted labels are highly effective - achieving an average attack success rate as high as 96.9%. We introduce a generalized attack framework targeting semi-supervised learning methods to better understand and exploit their limitations and to motivate future defense strategies.
